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https://api.github.com/repos/kubeflow/pipelines/issues/7095 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7095/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7095/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7095/events | https://github.com/kubeflow/pipelines/issues/7095 | 1,085,616,611 | I_kwDOB-71UM5AtTHj | 7,095 | test: error: deployment "ml-pipeline" exceeded its progress deadline | {
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] | 2021-12-21T09:26:06 | 2022-04-17T06:27:49 | null | CONTRIBUTOR | null | All sample/e2e tests are failing with either
* metadata-grpc deployment cannot rollout
* ml-pipeline deployment cannot rollout
because of connection timeout to the in-cluster mysql DB. | {
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"u... | closed | false | null | [] | null | [
"cc @chensun ",
"I also bumped into the exact same issue while testing the KF 1.5 RC0 manifests https://github.com/kubeflow/manifests/issues/2099\r\n\r\nI think this has definitely something to do with KinD, but I couldn't get to the bottom of it. For me it was:\r\n* KinD cluster with K8s 1.20.7\r\n* `1.8.0-rc.1`... | 2021-12-21T08:59:01 | 2022-02-08T08:13:42 | 2022-02-08T08:13:42 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
kind
* KFP version:
brunch `master`
### Steps to reproduce
follow README `kustomize`
https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/README.md
* error message
`"message": "invalid usage for client certificate: server auth",`
### describe csr
```
➜ .kind kubectl get csr cache-server.kubeflow
NAME AGE SIGNERNAME REQUESTOR CONDITION
cache-server.kubeflow 6m38s kubernetes.io/kube-apiserver-client system:serviceaccount:kubeflow:kubeflow-pipelines-cache-deployer-sa Approved,Failed
➜ .kind kubectl get csr cache-server.kubeflow -o json
{
"apiVersion": "certificates.k8s.io/v1",
"kind": "CertificateSigningRequest",
"metadata": {
"creationTimestamp": "2021-12-21T06:48:46Z",
"name": "cache-server.kubeflow",
"resourceVersion": "1485",
"uid": "bece32dd-b0f2-4d31-9e1c-2aafa656945e"
},
"spec": {
"extra": {
"authentication.kubernetes.io/pod-name": [
"cache-deployer-deployment-578ffc9d46-5bjml"
],
"authentication.kubernetes.io/pod-uid": [
"4866ee10-fb48-4034-9c36-bf519e0b81f1"
]
},
"groups": [
"system:serviceaccounts",
"system:serviceaccounts:kubeflow",
"system:authenticated"
],
"request": "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",
"signerName": "kubernetes.io/kube-apiserver-client",
"uid": "3796d0f3-65b1-443a-9b13-97e9a61e381b",
"usages": [
"digital signature",
"key encipherment",
"server auth"
],
"username": "system:serviceaccount:kubeflow:kubeflow-pipelines-cache-deployer-sa"
},
"status": {
"conditions": [
{
"lastTransitionTime": "2021-12-21T06:48:46Z",
"lastUpdateTime": "2021-12-21T06:48:46Z",
"message": "This CSR was approved by kubectl certificate approve.",
"reason": "KubectlApprove",
"status": "True",
"type": "Approved"
},
{
"lastTransitionTime": "2021-12-21T06:48:46Z",
"lastUpdateTime": "2021-12-21T06:48:46Z",
"message": "invalid usage for client certificate: server auth",
"reason": "SignerValidationFailure",
"status": "True",
"type": "Failed"
}
]
}
}
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7089 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7089/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7089/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7089/events | https://github.com/kubeflow/pipelines/issues/7089 | 1,084,398,941 | I_kwDOB-71UM5Aop1d | 7,089 | test: tests fail with "AccessDeniedException: 403 There is an account problem for the requested project." | {
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"Ohh, my guess was wrong. The project ml-pipeline-test itself is suspended.",
"Recovered"
] | 2021-12-20T06:11:50 | 2021-12-20T07:15:14 | 2021-12-20T07:15:14 | CONTRIBUTOR | null | This includes sample test, postsubmit tests.
However, samples-v2 test is still passing.
https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/7088/kubeflow-pipeline-sample-test/1472757856003428352
Therefore, my initial guess is that the image we use is too outdated, so that it can no longer authenticate with Google Cloud.
Looking at https://github.com/GoogleCloudPlatform/oss-test-infra/blob/18c1b811dfaf8b07d83dccd73120049991424750/prow/prowjobs/kubeflow/pipelines/kubeflow-pipelines-presubmits.yaml, most tests use gcr.io/k8s-testimages/kubekins-e2e:v20210113-cc576af-master image, but samples v2 test is using python:3.7 image. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7078 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7078/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7078/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7078/events | https://github.com/kubeflow/pipelines/issues/7078 | 1,082,387,910 | I_kwDOB-71UM5Ag-3G | 7,078 | [feature] Flexible tensorboard images | {
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"... | open | false | null | [] | null | [
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"any update on this? i have to include our antifactory images in our organization "
] | 2021-12-16T16:12:56 | 2022-09-14T15:38:34 | null | CONTRIBUTOR | null | ### Feature Area
/area frontend
### What feature would you like to see?
Allow installations to modify the images available on tensorboard. This would allow us to have images with tftext or tensorboard expansions as needed.
Currently tensorboard image list is a HTML embedded in the frontend.
### What is the use case or pain point?
Use tensorboard from KFP with different variations/versions and custom images.
### Is there a workaround currently?
Not possible, really manual deploy of an instance from kubectl.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7072 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7072/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7072/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7072/events | https://github.com/kubeflow/pipelines/issues/7072 | 1,081,883,211 | I_kwDOB-71UM5AfDpL | 7,072 | how to set restartPolicy "Never" to init containers | {
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"As far as I'm aware, we don't support setting restartPolicy for either main container nor init container via SDK. \r\nBTW, ContainerOp is deprecated, you should received a warning for the code above.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if... | 2021-12-16T08:01:33 | 2022-04-17T06:27:34 | null | NONE | null | ```
kfp.dsl.ContainerOp(
name = 'spark-test',
image = 'blahblah',
init_containers=[ kfp.dsl.UserContainer(name='sdfs', image='sdfs', command=['sh', '-c'], args=['sdfsf'])],
command = ["sh", "-c"])
```
this is my operation.
I tested wrong init containers and then pipeline retry my pipeline so many times.
I want to test just one try.
how to set restartPolicy to my pipeline container ? | {
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"pr #7065",
"Thank you for raising the issue.\r\n\r\nKFP is language-agnostic pipeline system. `None` is a python-specific object that is not present in other languages. Im'm not sure it's a good idea to tie KFP to a specific language.\r\n\r\n>the component recieve 'None'.\r\n\r\nKFP orchestrates containerized co... | 2021-12-15T09:13:20 | 2022-04-17T06:27:23 | null | NONE | null | When definining a kfp pipeline, with an str argument, if the value is 'None', then the component recieve 'None'.
Wouldn't be better, to parse str 'None' to None (NoneType)?
Now:
```py
@component
def task1(arg1: str = None):
if arg1 is None or arg1 == 'None':
print('arg1 is None')
@pipeline
pipeline(arg1: str = 'None'):
task1(arg1)
```
expected:
```py
@component
def task1(arg1: str = None):
if arg1 is None:
print('arg1 is None')
@pipeline
pipeline(arg1: str = 'None'):
task1(arg1)
``` | {
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"Maybe using argo workflows `argo_workflows_count` is a better way to track runs"
] | 2021-12-15T06:05:41 | 2021-12-15T06:55:59 | 2021-12-15T06:39:12 | CONTRIBUTOR | null | ### What steps did you take
1. Create a recurring run (seems to be called a Job in the backend?)
2. Enable the recurring run so that runs are generated
<!-- A clear and concise description of what the bug is.-->
### What happened:
- `job_server_job_count` correctly increases when creating a recurring run
- `run_server_run_count` does not increase when runs started by recurring runs occur
### What did you expect to happen:
I expected `run_server_run_count` to also increase for each run started by a recurring run so that I can track the total amount of runs. Currently that counter only increases from one-off runs.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? Initially Kubeflow 1.3 but we have since updated individual components
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.7
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"hi @Bobgy @zijianjoy, can I take this one? I'm looking for a first issue, to get familiar with the code."
] | 2021-12-14T01:20:58 | 2022-02-15T08:05:04 | null | CONTRIBUTOR | null | When a component gets input parameters, its input parameters may not exactly match the component input spec's requirements.
In such a case, we should consider adding auto type conversions for possible type mismatches.
Right now, string to other type conversions have been implemented at [code link](https://github.com/kubeflow/pipelines/blob/ca6e05591d922f6958a7681827aea25c41b94573/v2/driver/driver.go#L634-L703).
The issue tracks considering implementation of other type conversions like bool to int. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-12-13T04:07:19 | 2022-04-17T06:27:46 | null | CONTRIBUTOR | null | * [ ] P1 https://github.com/kubeflow/pipelines/issues/6161
* [ ] P2 https://github.com/kubeflow/pipelines/tree/master/samples/core/loop_parallelism | {
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"/lifecycle frozen\r\n\r\n"
] | 2021-12-13T03:02:29 | 2023-04-10T00:51:12 | 2023-04-10T00:51:11 | CONTRIBUTOR | null | KFP pipeline spec only has resource limit fields right now, consider also adding support for request fields.
https://github.com/kubeflow/pipelines/blob/1e032f550ce23cd40bfb6827b995248537b07d08/api/v2alpha1/pipeline_spec.proto#L632-L653 | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-12-13T03:01:31 | 2022-04-17T07:27:22 | null | CONTRIBUTOR | null | These samples should pass:
* [ ] https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_spec/runtime_resource_request.py
* [ ] https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_spec/runtime_resource_request_gpu.py | {
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"/lifecycle frozen"
] | 2021-12-12T14:34:37 | 2023-03-07T01:22:14 | 2023-03-07T01:22:14 | CONTRIBUTOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7040 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7040/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7040/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7040/events | https://github.com/kubeflow/pipelines/issues/7040 | 1,077,273,630 | I_kwDOB-71UM5ANeQe | 7,040 | [bug] Idempotency in kubeflow pipeline sagemaker component. | {
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"@akartsky @surajkota @mbaijal @ryansteakley FYI.",
"https://github.com/kubeflow/pipelines/issues/6465",
"/area components/aws/sagemaker",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contribution... | 2021-12-10T22:14:18 | 2022-04-28T04:59:41 | null | CONTRIBUTOR | null | ### What steps did you take
If node scales/up down, the sagemaker component tries to create the same job which fails. Since sagemaker does not let create the same name job. Component controller should be able to detect this and resume the job from existing state.
### What happened:
the job hangs/fail
### What did you expect to happen:
I expect the job to resume from previous state.
### Environment:
kfp-1.6
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7029 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7029/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7029/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7029/events | https://github.com/kubeflow/pipelines/issues/7029 | 1,075,415,737 | I_kwDOB-71UM5AGYq5 | 7,029 | [feature] upgrade MLMD to 1.5.0 | {
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"/assign @zijianjoy \r\n/cc @haoxins ",
"All clients for MLMD have been updated."
] | 2021-12-09T10:36:31 | 2022-01-01T19:26:12 | 2022-01-01T19:26:12 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
instructions: https://github.com/kubeflow/pipelines/tree/master/third_party/ml-metadata
Tasks
* [x] #6996
* [x] regenerate golang client
* [x] regenerate frontend client
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https://api.github.com/repos/kubeflow/pipelines/issues/7028 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7028/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7028/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7028/events | https://github.com/kubeflow/pipelines/issues/7028 | 1,075,363,752 | I_kwDOB-71UM5AGL-o | 7,028 | [feature] Make func_to_container_op functions ready for autogenerated docs | {
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"cc @chensun ",
"Hi @hahamark1, \r\n\r\n`func_to_container_op` function is actually deprecated. To generate the docstrings, we need some amount of work to generate kfp-specific docstrings. We will work on this in the future. Thanks!",
"Hi @ji-yaqi , came across this and was wondering whether there is a way user... | 2021-12-09T09:44:38 | 2022-02-17T20:26:00 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Currently the function wrapper (`func_to_container_op`) Does not copy the docstring of the function to the new wrapper object. Due to this, autogenerated docs cannot deal with kubeflow components.
### What is the use case or pain point?
It would help us drastically in spending less time on writing docs.
### Is there a workaround currently?
Currently, we write are docs by hand.
### How to solve it?
By adding the following to the function `func_to_container_op` in `components._python_op.py`
```
task_factory = _create_task_factory_from_component_spec(component_spec)
task_factory.__name__ = func.__name__
task_factory.__doc__ = func.__doc__
return task_factory
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7023 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7023/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7023/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7023/events | https://github.com/kubeflow/pipelines/issues/7023 | 1,074,912,007 | I_kwDOB-71UM5AEdsH | 7,023 | [sdk] Wrong IPython url links | {
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"cc @chensun ",
"Hi @juliadeclared I saw you closed the PR. Is the issue still valid? \r\n",
"@chensun yes, the issue is still valid. For KF installations, the generated URI should include domain/_/pipeline since this is how its mapped for KF full installation.\r\n\r\nMaybe this can be a small doc update or som... | 2021-12-08T22:49:30 | 2022-04-17T06:27:47 | null | NONE | null | ### Environment
Running on a full KF 1.4 installation on top of GKE.
* KFP version: 1.7.1
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.6.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.6.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.6.0
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
Create an experiment `kfp.Client.create_experiment() `in a notebook
Click the generated link in Ipython
### Expected result
<!-- What should the correct behavior be? -->
Currently generated link (incorrect): https://cluster_uri/#/experiments/details/88140703-5a6e-49ee-ba14-ed3b2a625877
Actual link (correct): https://cluster_uri/_/pipeline/?ns=namespace#/experiments/details/88140703-5a6e-49ee-ba14-ed3b2a625877
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7021 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7021/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7021/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7021/events | https://github.com/kubeflow/pipelines/issues/7021 | 1,074,060,393 | I_kwDOB-71UM5ABNxp | 7,021 | pod status is NotReady | {
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"Hello @ashissharma97 ,\r\n\r\nWould you like to get the events from failing pods to debug further\r\n\r\n```\r\nkubectl get <pod-id> -n <namespace> -o yaml\r\n```",
"Hi @zijianjoy,\r\nThanks for replying\r\nHere is the yaml file of the pod \r\n```\r\napiVersion: v1\r\nkind: Pod\r\nmetadata:\r\n labels:\r\n p... | 2021-12-08T06:30:36 | 2021-12-15T08:14:57 | 2021-12-15T08:14:57 | NONE | null | Hi,
I have installed Kubeflow from Kubeflow manifest and when I am running a pipeline I am getting pod status as “NotReady”. Can somebody help with this?
Thanks | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7015 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7015/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7015/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7015/events | https://github.com/kubeflow/pipelines/issues/7015 | 1,073,489,492 | I_kwDOB-71UM4__CZU | 7,015 | set component ram request using pipeline parameter value | {
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"cc @chensun ",
"@ypitrey The fundamental issue is that `300 * run_duration` is a piece of code not captured in any container. Generally speaking, a pipeline may only use components as building blocks, where each component is a containerized app. There can't be code hanging outside a component. Does that make sen... | 2021-12-07T15:31:08 | 2022-04-17T07:27:13 | null | NONE | null | I have a pipeline in which some components require an amount of RAM that is dependent on the run duration. Our runs can take anything from 10 minutes to 12 hours and we run mostly short runs (less than 30 minutes). I'm looking into adapting the memory request to have enough RAM for long runs while saving costs on short runs.
The run duration in hours is specified as a pipeline parameter. Basically, I'd like to do something like this:
```
@dsl.pipeline(name='example_pipeline', description='foo')
def pipeline(run_duration: float):
...
comp = my_component(run_duration)
comp.set_memory_request(f'{300 * run_duration}Mi') # 30 is an arbitrary value for this example
comp.set_memory_limit('4Gi') # '4Gi' is an arbitrary value for this example
...
```
But I can't do that because the value of `run_duration` isn't available from the `pipeline` function.
Is there a workaround for this? Or am I looking at the issue from the wrong perspective?
Thanks! | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7014 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7014/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7014/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7014/events | https://github.com/kubeflow/pipelines/issues/7014 | 1,073,316,650 | I_kwDOB-71UM4_-YMq | 7,014 | [backend] Forbid unarchive runs action if the run belongs to archived experiment | {
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"/assign @difince ",
"This behavior sounds good to us, feel free to contribute to this fix!"
] | 2021-12-07T12:51:16 | 2022-02-09T23:24:33 | 2022-02-09T23:24:33 | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
</area backend>
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Add backend validation so that the user cannot activate a Run if it belongs to Archived Experiment.
When [unarchive](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs--id-:unarchive-post) endpoint is called, check if the Run is under an **Archived** Experiment. If so, return an error 412 Precondition Failed
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
In this [issue-comment](https://github.com/kubeflow/pipelines/issues/5114#issuecomment-777735817), it was concluded that **Archived Experiment** _cannot_ contain **Active Runs**. This raises the need for such validation.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7013 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7013/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7013/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7013/events | https://github.com/kubeflow/pipelines/issues/7013 | 1,073,217,547 | I_kwDOB-71UM4_-AAL | 7,013 | [sdk] Compile fails when the pipeline name is not specified | {
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"cc @chensun ",
"Hi @ysk24ok, in V2, we ask that pipeline name should be specified for better usage. \r\n\r\nYour code should look like:\r\n```\r\n@pipeline(name=\"your_pipeline_name\")\r\ndef sample():\r\n do_nothing_op()\r\n```\r\n\r\nHope this explains!\r\n",
"@ji-yaqi Thank you for your response.\r\nThen... | 2021-12-07T11:05:12 | 2022-08-29T11:22:11 | 2022-08-29T11:22:10 | CONTRIBUTOR | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.8.9
kfp-pipeline-spec 0.1.13
kfp-server-api 1.7.1
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```console
$ cat pipelines.py
from kfp.v2.dsl import pipeline, component
from kfp.v2.compiler import Compiler
@component
def do_nothing_op():
pass
@pipeline()
def sample():
do_nothing_op()
Compiler().compile(
pipeline_func=sample,
package_path="/tmp/pipeline.json",
)
$ python3 pipelines.py
Traceback (most recent call last):
File "pipelines.py", line 17, in <module>
Compiler().compile(
File "/home/ysk24ok/repos/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 94, in compile
pipeline_spec = self._create_pipeline_v2(
File "/home/ysk24ok/repos/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 184, in _create_pipeline_v2
pipeline_spec = self._create_pipeline_spec(
File "/home/ysk24ok/repos/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 275, in _create_pipeline_spec
self._validate_pipeline_name(pipeline.name)
File "/home/ysk24ok/repos/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 393, in _validate_pipeline_name
raise ValueError(
ValueError: Invalid pipeline name: Sample.
Please specify a pipeline name that matches the regular expression "^[a-z0-9][a-z0-9-]{0,127}$" using `dsl.pipeline(name=...)` decorator.
```
### Expected result
<!-- What should the correct behavior be? -->
The pipeline can be compiled without specifying the name of the pipeline.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://github.com/kubeflow/pipelines/blob/c1b67154a2abb75bd7dea4d2c8c52a52e2e8f7b2/sdk/python/kfp/v2/compiler/compiler.py#L129
It seems this bug is caused here.
If the pipeline name is not specified, `pipeline_name` is None and `pipeline_meta.name` is used as its name, which is the return value of `_python_function_name_to_component_name`.
https://github.com/kubeflow/pipelines/blob/c1b67154a2abb75bd7dea4d2c8c52a52e2e8f7b2/sdk/python/kfp/v2/components/component_factory.py#L289-L291
This function capitalzies the first character and it does not match the rule in the compiler.
So I think we should add a new function like `_python_function_name_to_pipeline_name` and use the result instead of `pipeline_meta.name`. Since the rule of components name and that of the pipeline name is different, we should use different functions.
```
pipeline_name = pipeline_name or _python_function_name_to_pipeline_name(pipeline_func)
```
If this approach is appropriate, I'll work on this.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7012 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7012/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7012/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7012/events | https://github.com/kubeflow/pipelines/issues/7012 | 1,072,589,464 | I_kwDOB-71UM4_7mqY | 7,012 | [google-cloud-pipeline-components] Job Name in gcp_resource is not formatted correctly or is empty | {
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"cc @IronPan ",
"kfp -> 1.8.10\r\ngoogle-cloud-pipeline-components -> 0.2.1\r\n\r\nI'm experiencing the same issue when running tasks created from `google_cloud_pipeline_components.experimental.hyperparameter_tuning_job` \r\nAnd I located to the same place `google_cloud_pipeline_components\\container\\experimenta... | 2021-12-06T20:38:14 | 2022-08-31T13:33:23 | null | NONE | null | ### Environment
* KFP SDK version: 1.8.9
* All dependencies version:
google_cloud_pipepline_components version: 0.2.0
### Steps to reproduce
Followed the tutorial of the Vertex AI pipeline and created a training job with the code below
```
training_op = gcc_aip.CustomContainerTrainingJobRunOp(
display_name = "test",
container_uri=container_uri,
project = project,
location = gcp_region,
dataset = dataset_create_op.outputs["dataset"],
staging_bucket = bucket,
training_fraction_split = 0.8,
validation_fraction_split = 0.1,
test_fraction_split = 0.1,
model_serving_container_image_uri = "custom container image uri",
model_serving_container_health_route="/healthcheck",
model_serving_container_predict_route="/predict",
model_display_name = "scikit-tests",
machine_type = "n1-standard-4",
)
```
After submitting the pipeline job, I received the error "Job Name in gcp_resource is not formatted correctly or is empty". By checking the source code, I suspect there is a bug in `google_cloud_pipeline_components\container\experimental\gcp_launcher\job_remote_runner.py` at line 80
The source code is
```
job_name_group = re.findall(
job_resources.resources[0].resource_uri,
f'{self.job_uri_prefix}(.*)')
```
To the best of my knowledge, the signature for `re.findall` is `re.findall(pattern, string, flags=0)` [python doc](https://docs.python.org/3/library/re.html#re.findall), which indicates that the first parameter should be "pattern" while in the code snippet above it looks like you are using the "string" as the first parameter.
Could you please confirm if that is expected?
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https://api.github.com/repos/kubeflow/pipelines/issues/7009 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7009/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7009/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7009/events | https://github.com/kubeflow/pipelines/issues/7009 | 1,072,199,648 | I_kwDOB-71UM4_6Hfg | 7,009 | [backend] kubeflow 1.6.0 no support onExit | {
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"here is our test pipeline\r\n\r\n\r\n\r\n```\r\nimport os\r\nimport sys\r\n\r\nimport kfp.compiler\r\nimport kfp.components as comp\r\nimport kfp.dsl as dsl\r\nimport json\r\n\r\nstart_comp = comp.load_component_from_file('./start.yaml')\r\nwork_comp = comp.load_component_from_file('./work.yaml')\r\nexit_comp = co... | 2021-12-06T14:12:56 | 2022-10-07T00:54:16 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
kubeflow 1.6.0
### Steps to reproduce
I write a pipeline with on exit component and I can upload to kubeflow 1.0, but when I use 1.6.0 kubeflow,
but when comment the
it can be upload to kubeflow 1.6.0 and missing the datasync component
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"Maybe the following methods could be useful too, especially for testing and debugging purposes:\r\n\r\n```python\r\n@abstractmethod\r\ndef get_all_globals(self) -> Mapping[str, Any]: ...\r\n\r\n@abstractmethod\r\ndef reset_globals(self) -> None: ...\r\n\r\n@abstractmethod\r\ndef set_globals_map(self, values: Mappi... | 2021-12-06T12:29:31 | 2022-04-26T23:21:53 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Gathering all the mutable globals used during KFP compilation into one singleton. That way, the users could implement their own implementation to support any compilation state model they need, instead of a synchronous global-variables-based approach that is currently used, and trivially set it in a single one, globally replacing one singleton with another. Thanks to that, third-party plugins could provide KFP SDK support for frameworks such as Flask, Django or others, independently from the KFP itself.
At the same time, the singleton doesn't need to be aware of all the state needed by KFP, but rather provide a generic mechanism of setting and retrieving the values for globals via unique keys (names?).
### What is the use case or pain point?
Currently, KFP compilation requires using numerous global variables such as `Pipeline._default_pipeline`, `_components._container_task_constructor`, `_container_op._register_op_handler` --- which means that at least the [`compile_pipeline` function must be @synchronized (locked) between calls](https://github.com/kubeflow/kfp-tekton/blob/master/sdk/python/tests/performance_tests.py#L108-L134). This is very ineffective in case of using KFP DSL compilation in concurrent environments, which could be trivially avoided by moving the global state to some proxy.
### Is there a workaround currently?
One could try to manually overwrite each individual global private variable mutated in KFP with proxies, but it has many disadvantages to it: it's not only very hacky, but because they're private, they can be removed or added at any time in subsequent versions of KFP.
### Implementation details
I imagine that the base class of such global context may look as follows:
```python
class KfpCompilationCtx:
@abstractmethod
def set_global(self, name: str, value: Any) -> None: ...
@abstractmethod
def get_global(self, name: str) -> Any: ...
def get_set_global(self, name: str, value: Any) -> Any:
try:
old_value = self.get_global(name)
except:
old_value = None
self.set_global(name, value)
return old_value
# could also be a static inside of the class, but that would enable other classes to overwrite it
_kfp_ctx: 'KfpCompilationCtx' = None
def set_kfp_ctx(ctx: KfpCompilationCtx):
global _kfp_ctx
_kfp_ctx = ctx
def get_kfp_ctx() -> KfpCompilationCtx:
return _kfp_ctx
class KfpGlobalCtx(KfpCompilationCtx):
def __init__(self):
super().__init__(self)
self._globals_dict: Dict[str, Any] = {}
def set_global(self, name: str, value: Any) -> None:
self._globals_dict[name] = value
def get_global(self, name: str) -> Any:
return self._globals_dict[name]
# by default, the context is global-based
set_kfp_ctx(KfpGlobalCtx())
```
but it would also trivially enable the following implementation for Flask:
```python
from flask import g
class KfpFlaskCtx(KfpCompilationCtx):
def set_global(self, name: str, value: Any) -> None:
setattr(g, name, value)
def get_global(self, name: str) -> Any:
return getattr(g, name)
```
Now, the current implementation of `Pipeline` class would change to:
```python
class Pipeline():
def __init_(self, name: str):
self.name = name
self.ops = {}
# Add the root group.
self.groups = [_ops_group.OpsGroup('pipeline', name=name)]
self.group_id = 0
self.conf = PipelineConf()
self._metadata = None
@staticmethod
def get_default_pipeline() -> Optional['Pipeline']:
return get_kfp_ctx().get_global('default_pipeline')
@staticmethod
def _set_default_pipeline(value: Optional['Pipeline']) -> None:
return get_kfp_ctx().set_global('default_pipeline', value)
def __enter__(self) -> 'Pipeline':
if self.get_default_pipeline():
raise Exception('Nested pipelines are not allowed.')
self._set_default_pipeline(self)
ctx = get_kfp_ctx()
self._old_container_task_constructor = ctx.get_set_global(
'container_task_constructor',
_component_bridge._create_container_op_from_component_and_arguments
)
def register_op_and_generate_id(op):
return self.add_op(op, op.is_exit_handler)
self._old__register_op_handler = ctx.get_set_global(
'register_op_handler',
register_op_and_generate_id
)
return self
def __exit__(self, *args):
self._set_default_pipeline(None)
ctx = get_kfp_ctx()
ctx.set_global(self._old__register_op_handler)
ctx.set_global(self._old_container_task_constructor)
...
```
...which is trivially compatible with both implementations.
Considering how in KFP it's usually only required to set and unset some global, maybe `set_global` and `unset_global` would be better instead --- certainly would be easier to implement global locking mechanisms. Maybe a `setdefault` could be useful etc. But the general idea is that of the above.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7003 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7003/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7003/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7003/events | https://github.com/kubeflow/pipelines/issues/7003 | 1,070,469,896 | I_kwDOB-71UM4_zhMI | 7,003 | [feature] Add GetPipelineByName endpoint | {
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"/assign @difince ",
"/assign @capri-xiyue ",
"pipeline name is not unique in KFP. I think we can make the combination of name and namespace unique in KFP. FYI: https://github.com/kubeflow/pipelines/issues/3360#issuecomment-834450654",
"Thank you for clarifying this @capri-xiyue. \r\nThis issue is actually in... | 2021-12-03T11:00:30 | 2022-05-19T07:29:53 | 2022-02-15T17:38:41 | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Add new endpoint - getPipelineByName
Because the pipeline name is unique within a namespace - the namespace should be taken into consideration
### What is the use case or pain point?
Simplify user experience and keep consistent with other tools like Kubernetes, DAGs in Argo, Airflow that use names as a reference instead of UUID
This issue is inspired from #https://github.com/kubeflow/pipelines/issues/3360.
### Is there a workaround currently?
Yes, use UUIDs instead
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/6987 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6987/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6987/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6987/events | https://github.com/kubeflow/pipelines/issues/6987 | 1,068,023,708 | I_kwDOB-71UM4_qL-c | 6,987 | [bug] upgrade from MLMD 1.0.0 to 1.4.0 stuck in crashloopbackoff | {
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"I tried reproducing this issue with a brand new MLMD grpc server, but I wasn't able to reproduce.",
"I'm going to delete the MLMD db manually to unblock the upgrade for development in https://github.com/kubeflow/pipelines/pull/6983",
"I connected to in-cluster mysql and deleted MLMD db in kfp-ci cluster.\r\n``... | 2021-12-01T06:43:11 | 2023-03-21T17:52:11 | 2023-03-21T17:52:11 | CONTRIBUTOR | null | Upstream issue: https://github.com/google/ml-metadata/issues/135 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6986 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6986/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6986/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6986/events | https://github.com/kubeflow/pipelines/issues/6986 | 1,067,607,765 | I_kwDOB-71UM4_ombV | 6,986 | [sdk] v2 Components not supported by LocalClient | {
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"This is known and expected. `run_pipeline_func_locally` was added as an alpha feature by a community contributor. It targets v1 only and has many limitations. \r\nThere's currently no plan to make it work for v2 components and pipelines.",
"@chensun @gpoulin-hopper Actually I have implemented the local client f... | 2021-11-30T19:41:45 | 2022-04-24T10:32:45 | null | NONE | null | ### Environment
* KFP version:
N/A
* KFP SDK version:
1.8.9
* All dependencies version:
```
kfp 1.8.9
kfp-pipeline-spec 0.1.13
kfp-server-api 1.7.1
```
### Steps to reproduce
Running this python script
```python3
import kfp
import kfp.v2.dsl as dsl
@dsl.component()
def simple():
print("allo")
@dsl.pipeline(name="test", description="test")
def pipeline():
simple()
kfp.run_pipeline_func_locally(pipeline, {})
```
produce:
```
ERROR:root:WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/executor_main.py", line 104, in <module>
executor_main()
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/executor_main.py", line 94, in executor_main
executor_input = json.loads(args.executor_input)
File "/usr/local/lib/python3.7/json/__init__.py", line 348, in loads
return _default_decoder.decode(s)
File "/usr/local/lib/python3.7/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/lib/python3.7/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
ERROR:root:['docker', 'run', '-v', '/var/folders/99/4l_08z0d4yvfc3ywqvh42pr00000gq/T:/var/folders/99/4l_08z0d4yvfc3ywqvh42pr00000gq/T', 'python:3.7', 'sh', '-c', '\n\nif ! [ -x "$(command -v pip)" ]; then\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location \'kfp==1.8.9\' && "$0" "$@"\n', 'sh', '-ec', 'program_path=$(mktemp -d)\nprintf "%s" "$0" > "$program_path/ephemeral_component.py"\npython3 -m kfp.v2.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"\n', '\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef simple():\n print("allo")\n\n', '--executor_input', '{{$}}', '--function_to_execute', 'simple']
```
### Expected result
To have the same result as if `simple` was a v1 component
```python3
import kfp
from kfp.components import create_component_from_func
import kfp.dsl as dsl
@create_component_from_func
def simple():
print("allo")
@dsl.pipeline(name="test", description="test")
def pipeline():
simple()
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6981 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6981/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6981/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6981/events | https://github.com/kubeflow/pipelines/issues/6981 | 1,066,746,703 | I_kwDOB-71UM4_lUNP | 6,981 | Argument type "google.VertexDataset" is incompatible with the input type "Dataset" | {
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] | open | false | null | [] | null | [
"any update on this? I am encountering something similar with using the output of Vertex AI model upload, so I have a `google.VertexModel` that does not match `Input[Model]` in a python component. ",
"same problem with the VertexModel\r\nhave a customop defined:\r\n\r\nfrom kfp.v2.dsl import (\r\n component,\r... | 2021-11-30T03:29:19 | 2022-03-01T13:11:44 | null | NONE | null | ### What steps did you take
I created a KF pipeline in which:
1. First created a Vertex AI dataset
2. Defined a custom component, and pass the dataset as an input.
I was expecting that the dataset_create_task.outputs["dataset"] would pass a dataset to run_hptune as input. However, I got this error:
'Argument type "google.VertexDataset" is incompatible with the input type "Dataset"'
`def run_hptune(
dataset: Input[Dataset],
hypertune_display_name: str,
image_uri: str,
staging_bucket: str)->int:`
`@dsl.pipeline(name=pipeline_name, pipeline_root='gs://xxx')
def pipeline():
dataset_create_task = gcc_aip.TabularDatasetCreateOp(
project=project_id,
display_name=model_display_name,
gcs_source=gcs_resource_uri
)
hypertune_job_run_task = run_hptune(
dataset=dataset_create_task.outputs["dataset"],
hypertune_display_name=hypertune_display_name,
image_uri=container_uri,
staging_bucket=staging_bucket)`
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https://api.github.com/repos/kubeflow/pipelines/issues/6977 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6977/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6977/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6977/events | https://github.com/kubeflow/pipelines/issues/6977 | 1,065,950,466 | I_kwDOB-71UM4_iR0C | 6,977 | [feature] Provide a way to define a set of default annotations for tasks | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | open | false | null | [] | null | [
"Do you need to customize the annotation for pipeline tasks? For example, KFP provides a interface for user to define the annotations for specific pipeline tasks?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you... | 2021-11-29T11:57:39 | 2022-03-03T02:05:10 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
We would like to be able to define a set of annotations on the server-side, that will be automatically added to all pipeline tasks.
### What is the use case or pain point?
We need this feature to enable Istio sidecar injection by default to all the submitted pipelines. We want by default to annotate all tasks with:
```yaml
sidecar.istio.io/inject: true
proxy.istio.io/config: '{ "holdApplicationUntilProxyStarts": true }'
```
Currently, all tasks are automatically annotated with `sidecar.istio.io/inject: false` and we need a way to change this default behavior.
### Is there a workaround currently?
The current workaround is to explicitly set the annotations in each pipeline task.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6974 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6974/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6974/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6974/events | https://github.com/kubeflow/pipelines/issues/6974 | 1,065,592,462 | I_kwDOB-71UM4_g6aO | 6,974 | [bug] TFX sample fails after upgrading to 1.4 | {
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"followers_url": "https://a... | null | [
"/assign @zijianjoy @jiyongjung0 ",
"@Bobgy: GitHub didn't allow me to assign the following users: jiyongjung0.\n\nNote that only [kubeflow members](https://github.com/orgs/kubeflow/people), repo collaborators and people who have commented on this issue/PR can be assigned. Additionally, issues/PRs can only have 1... | 2021-11-29T04:18:29 | 2021-12-23T07:59:05 | 2021-12-02T10:00:06 | CONTRIBUTOR | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
* MLMD & TFX upgrade: https://github.com/kubeflow/pipelines/pull/6910
### What happened:
* Check test: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1464225919869652992
### What did you expect to happen:
We need to upgrade the TFX sample to 1.4.0
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
/area samples
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6972 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6972/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6972/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6972/events | https://github.com/kubeflow/pipelines/issues/6972 | 1,065,193,798 | I_kwDOB-71UM4_fZFG | 6,972 | [backend] Create Run in KFPv2 failing because of invalid name in argo workflow | {
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"u... | closed | false | null | [] | null | [
"I did some investigation, the root cause is because when creating pipeline, the pipeline api will override the name to pipeline/xxx format, FYI \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/backend/src/apiserver/resource/resource_manager.go#L263https://github.com/kubef... | 2021-11-28T05:36:41 | 2021-12-08T05:48:59 | 2021-12-08T05:48:59 | COLLABORATOR | null | During KFPv2, I tried to create KFPv2 run using existing pipeline template. The following is the request payload for create run:
```
{
"description":"",
"name":"Run of v2upload (6792a)",
"pipeline_spec":{
"parameters":[
]
},
"resource_references":[
{
"key":{
"id":"ba85c817-a301-4f9c-920d-b654c4a03c81",
"type":"EXPERIMENT"
},
"relationship":"OWNER"
},
{
"key":{
"id":"3943f4b9-2d33-487c-8a6e-0a17fbeff62c",
"type":"PIPELINE_VERSION"
},
"relationship":"CREATOR"
}
],
"service_account":""
}
```
The error is:
Looks like the run name pattern `pipeline/v2upload-8d5ks` didn't pass the argo workflow validation because it shouldn't contain `/` symbol.
cc @Bobgy @capri-xiyue | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6970 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6970/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6970/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6970/events | https://github.com/kubeflow/pipelines/issues/6970 | 1,064,850,622 | I_kwDOB-71UM4_eFS- | 6,970 | [bug] Link on KubeFlow website is 404 | {
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"Thank you for the report! I created the fix https://github.com/kubeflow/website/pull/3081"
] | 2021-11-26T23:16:56 | 2021-11-28T12:58:04 | 2021-11-28T12:58:04 | NONE | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
Visit:
https://www.kubeflow.org/
Click on the KubeFlow Pipelines link, which direct me to
https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/
### What happened:
I see a 404 page.
### What did you expect to happen:
display a page with contents.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6966 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6966/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6966/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6966/events | https://github.com/kubeflow/pipelines/issues/6966 | 1,064,130,065 | I_kwDOB-71UM4_bVYR | 6,966 | [sdk] component with optional input causes compile error in v2 compiler | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-26T06:03:28 | 2022-03-02T10:06:22 | null | CONTRIBUTOR | null | ### Environment
* KFP SDK version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
* Migrate if_placeholder sample pipeline to v2: https://github.com/kubeflow/pipelines/blob/8aee62142aa13ae42b2dd18257d7e034861b7e5e/samples/test/placeholder_if.py by changing imports to `from kfp.v2 import components, dsl`
* Compile
* Got this error
```
Traceback (most recent call last):
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/components/structures.py", line 579, in load_from_component_yaml
return ComponentSpec.parse_obj(json_component)
File "pydantic/main.py", line 578, in pydantic.main.BaseModel.parse_obj
File "pydantic/main.py", line 406, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ComponentSpec
inputs
value is not a valid dict (type=type_error.dict)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/gongyuan_kubeflow_org/miniconda3/envs/v2/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/gongyuan_kubeflow_org/miniconda3/envs/v2/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/samples/test/placeholder_if_test.py", line 17, in <module>
from .placeholder_if_v2 import pipeline_both as pipeline_both_v2, pipeline_none as pipeline_none_v2
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/samples/test/placeholder_if_v2.py", line 46, in <module>
''')
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/components/yaml_component.py", line 36, in load_component_from_text
structures.ComponentSpec.load_from_component_yaml(text))
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/components/structures.py", line 583, in load_from_component_yaml
return cls.from_v1_component_spec(v1_component)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/components/structures.py", line 500, in from_v1_component_spec
for spec in component_dict.get('outputs', [])
File "pydantic/main.py", line 406, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ComponentSpec
__root__
'NoneType' object is not iterable (type=type_error)
```
### Expected result
<!-- What should the correct behavior be? -->
1. Should this be supported?
2. If not, can we have a clearer error message?
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6956 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6956/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6956/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6956/events | https://github.com/kubeflow/pipelines/issues/6956 | 1,062,667,067 | I_kwDOB-71UM4_VwM7 | 6,956 | run vertex pipeline with py-func locally | {
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"node_id": "MDU6TGFiZWwyNzEwMTU4MTQ3"... | open | false | null | [] | null | [
"An start step would be next code playing with the source of the code to execute it dynamically. But would only work with single output's and you still need to import properly the components as functions (which is verbose)\r\n\r\nstart_step could be confusing and out of the current scope but since this is for debug... | 2021-11-24T17:01:06 | 2023-05-02T17:23:30 | null | NONE | null | For debugging, in kfp.V2, would be good to run the vertex_pipeline based on py-funcs you define locally (without docker build)
Also would avoid creating an almost identical, pipeline_local_func function.
For example with an
```py
from kfp.v2 import compiler
pipeline_filename = 'tmp/kfp_pipeline.json'
compiler.Compiler().compile(pipeline_func=pipeline_func,
package_path=pipeline_filename)
job = aiplatform.PipelineJob(
pipeline_filename ,
local = True)
job.run()
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6953 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6953/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6953/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6953/events | https://github.com/kubeflow/pipelines/issues/6953 | 1,061,853,986 | I_kwDOB-71UM4_Spsi | 6,953 | doc: rename compatibility matrix page to tfx? | {
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"description": "The issue / pull request is stale, any activities remove this label.... | open | false | null | [] | null | [
"Thank you for creating this issue!\r\n\r\n\r\nFor the discussion of this doc title, I have a couple questions:\r\n\r\n1. Is there any plan for compatibility matrix beyond tfx in the future? (Say PytorchX, MLMD, argo, etc.)\r\n2. How does user find the right combination of ML frameworks+KFP nowadays? (Or how do we ... | 2021-11-24T00:39:36 | 2022-03-02T10:06:20 | null | CONTRIBUTOR | null | @zijianjoy @kramachandran @Bobgy in the interest of getting this merged, I have removed that rename from this PR.
However, I still think we should discuss renaming that page in the future.
_Originally posted by @thesuperzapper in https://github.com/kubeflow/website/pull/3063#discussion_r755575916_ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6949 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6949/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6949/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6949/events | https://github.com/kubeflow/pipelines/issues/6949 | 1,061,427,264 | I_kwDOB-71UM4_RBhA | 6,949 | [bug] Conditions used to select component | {
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"I encountered a similar issue (but not the same).\r\nWhen I ran:\r\n```\r\n@func_to_container_op\r\ndef op_0(flag: bool) -> bool:\r\n return flag\r\n\r\n@func_to_container_op\r\ndef op_1(flag: bool) -> bool:\r\n return flag\r\n\r\n@func_to_container_op\r\ndef op_2(flag: bool) -> bool:\r\n return flag\r\n\... | 2021-11-23T15:35:36 | 2023-01-31T04:07:36 | null | NONE | null | ### What steps did you take
I am trying to use a pipeline to train two types of models (which are in different frameworks). I select which model type to train through a boolean pipeline argument. When selecting `Model_A`, I want to run a data preparation step for `Model_A`, and when selecting `Model_B` I want to run its respective data preparation step. I am using `dsl.Condition` to perform these steps.
After either data preparation step, I want to run a data export step, which is the same for both models. I place this step after the conditional steps for the data preparation.
See below an example script:
```
import kfp
import kfp.dsl as dsl
from kfp import compiler
from kfp.components import create_component_from_func
@create_component_from_func
def add1(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
print(a + b)
return a + b
@create_component_from_func
def add2(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
print(a + b)
return a + b
@create_component_from_func
def add3(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
print(a + b)
return a + b
@dsl.pipeline(
name='addition-pipeline',
description='An example pipeline that performs addition calculations.',
)
def add_pipeline(a: float, b: float,
c: float, d: float,
is_true: bool = True
):
add_task = add1(a, b)
with dsl.Condition(is_true == True, name='is-True'):
add_task_2 = add2(c, d)
with dsl.Condition(is_true == False, name='is-False'):
add_task_2 = add2(a, c)
add_task_3 = add3(a, add_task_2.output)
client = kfp.Client()
client.create_run_from_pipeline_func(
add_pipeline,
arguments={'a': 1, 'b': 2, 'c': 5, 'd': 12, 'is_true': False},
)
```
This script has a similar structure to the problem I am trying to solve: First, I perform an addition of two numbers (which are selected through the boolean flag), then I use the output of the addition in a following step.
<!-- A clear and concise description of what the bug is.-->
### What happened:
An error is triggered in the UI:
`invalid spec: templates.addition-pipeline.tasks.condition-is-False-2 templates.condition-is-False-2.outputs failed to resolve {{tasks.add2-2.outputs.parameters.add2-2-Output}}`
And the graph corresponding to the pipeline shows that `Add3` would not be started if the condition is True.
### What did you expect to happen:
The pipeline should not trigger an error, and `Add3` should always be dependent on the output of `Add2`.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
As part of a KubeFlow installation.
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
1.5.0
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
1.8.9
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
It seems that `kfp` does not reference the first `Add2` as an input to `Add3`, as if it is being overwritten by the second `Add2`. Is the `Condition` class not made for use cases such as this? In the examples, it is only used for leaf nodes of the DAG, but our use case does not seem very far-fetched.
It would be great if someone can shed some light on this.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6948 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6948/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6948/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6948/events | https://github.com/kubeflow/pipelines/issues/6948 | 1,061,282,880 | I_kwDOB-71UM4_QeRA | 6,948 | [frontend] taskname instead of component name in pipeline view or rename components on use | {
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"Well. A colleague found the undocumented solution. In case someone else wonders how you can rename the task:\r\n\r\n```\r\n task_get_model = op_get_minio_file(\r\n minio_uri=minio_model_uri\r\n )\r\n task_get_model.apply(use_aws_secret(\r\n 'mlpipeline-minio-artifact',\r\n 'accesskey'... | 2021-11-23T13:22:18 | 2021-11-23T13:31:43 | 2021-11-23T13:31:42 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? via manifests
* KFP version: 1.8.9
### Steps to reproduce
- create a pipeline using the same component twice (different runtime parameters)
### Expected result
- I would expect the task-name instead of the component name within kubeflow pipelines UI
- I'd like to distinct the usage of components. E.g. write statistics for validation/test or loading model/dataset
### Materials and Reference
- is it possible to plot the taskname instead of the component name?
- or how do I rename the component?
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6940 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6940/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6940/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6940/events | https://github.com/kubeflow/pipelines/issues/6940 | 1,060,452,705 | I_kwDOB-71UM4_NTlh | 6,940 | [feature] Publish a wheel for package `kfp` | {
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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-22T18:09:08 | 2022-03-02T10:06:24 | null | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Please publish a built distribution (wheel) for the package `kfp`.
https://packaging.python.org/guides/analyzing-pypi-package-downloads/ says both files (source distribution and source archive) get created by default if you use the PyPA package `build`:
```
python3 -m pip install --upgrade build
python3 -m build
```
### What is the use case or pain point?
I am one of the maintainers of `google-auth` and `google-cloud-*` libraries. This summer we released a v2 of `google-auth` and `google-api-core`, which we anticipated would cause diamond dependency conflicts for folks using multiple dependent packages. To mitigate this I proactively opened PRs like #6939 on many repositories.
I was able to quickly discover dependent packages through the [PyPI Bigquery Dataset](https://packaging.python.org/guides/analyzing-pypi-package-downloads/). This dataset lists the `install_requires` as long as the package has published a wheel. https://dustingram.com/articles/2018/03/05/why-pypi-doesnt-know-dependencies/ explains in more detail why the wheel is needed to get this data.
The wheel also makes it possible for websites like https://libraries.io/ to list dependents and dependencies of packages. Notice that https://libraries.io/pypi/kfp lists 0 dependencies. For comparison see https://libraries.io/pypi/google-cloud-vision.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
I used two additional strategies to try to find dependent packages:
1. Looking through GItHub. This is more manageable when the search is scoped to a single organization, but gets more difficult when packages are in many organizations.
2. Ask folks on partner teams if they know of any projects that might depend directly on `google-auth`.
Both options are imperfect and require human judgement.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6936 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6936/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6936/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6936/events | https://github.com/kubeflow/pipelines/issues/6936 | 1,059,589,930 | I_kwDOB-71UM4_KA8q | 6,936 | avoid declaring two times same function (and params) in vertex-pipeline with py-functions | {
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"PR #7049",
"IIUC, what you want to achieve after the PR is something like this:\r\n\r\n```python\r\nfrom kfp_test.test import test\r\n \r\ntest_op = create_component_from_func(func=test, base_image=base_image)\r\n```\r\n\r\nThere're a couple of issues with this approach:\r\n1. It requires installing the `kfp_tes... | 2021-11-22T02:04:07 | 2022-04-17T06:27:26 | null | NONE | null | context:
With vertex-pipeline + py-functions We can use a base_image (docker) with our py library pre-installed. Therefore the lib functions are available to import inside the pipeline.
problem:
Assuming we want to use one of this functions (without any change) in the pipeline, is a little verbose to declare with @component the same function again, which is corresponding paramater types.
Would be cleaner if you could just declare it once.
And the way the code works, wouldn't be so much of a change.
solution proposal:
assuming our function is in our library:
`from kfp_test.test import test`
then we have the option
1) clean but verbose
```
@component(base_image=base_image)
def test(x: int) -> int:
kwargs = locals()
from kfp_test.test import test
return test(**kwargs)`
```
2) not clean but not verbose
```
from inspect import getsource as getsource2
# deal with line 93 in component_factory.py: func_code = inspect.getsource(func)
def getsource(func):
if 'func_code' in func.__dict__.keys():
return func.func_code
else:
return getsource2(func)
from kfp.v2 import compiler
(...)
from kfp_test.test import test
func_code = f' def {inspect.signature(test)}:\n kwargs = locals()\n from kfp_test.test import test\n return test(**kwargs)\n'
test.func_code = func_code
test = create_component_from_func(func=test, base_image=base_image)
```
if we had a function in kfp like `create_component_from_func_code` that handled option 2 natively (create a wrapper string automatically from the func and ingestit in the create_component() without declaring it) , then we would have a non-verbose, clean-way to use functions from the library inside the pipeline
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https://api.github.com/repos/kubeflow/pipelines/issues/6935 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6935/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6935/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6935/events | https://github.com/kubeflow/pipelines/issues/6935 | 1,058,923,115 | I_kwDOB-71UM4_HeJr | 6,935 | [feature] Provide a way to filter for currently enabled recurring runs | {
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"This sounds like a useful feature to add. \r\n\r\nHello Xiyue, would you like to take a look and see if we are able to support filtering by job status from backend? Reference: https://github.com/kubeflow/pipelines/blob/949cfdca3794416210805ca1579adbd44ebec592/backend/src/apiserver/storage/job_store.go#L59\r\n\r\n/... | 2021-11-19T21:16:13 | 2022-04-21T00:22:01 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
The recurring runs frontend page could be more useful for my teams if we could filter for just the currently active recurring runs. A checkbox/button would be great for this. Alternatively, just being able to sort by the "Status" column could be an easy way to implement this.
### What is the use case or pain point?
When my team merges PRs, our CI disables the previous production recurring runs and schedules new runs with the latest code, so there are a lot of recurring runs. It would be helpful for us to understand what recurring runs are currently active.
### Is there a workaround currently?
Perhaps we could use the KFP API client to get this? But no workaround in the frontend UI.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6931 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6931/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6931/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6931/events | https://github.com/kubeflow/pipelines/issues/6931 | 1,058,627,123 | I_kwDOB-71UM4_GV4z | 6,931 | How to use docker images from a private registry | {
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"How do you solve this problem?",
"For version 1.4 from what I see driver image is hardcoded in driver.go "
] | 2021-11-19T15:04:47 | 2022-01-22T20:01:28 | 2021-11-30T13:55:30 | NONE | null | Hello! We are trying to deploy KFP in an enterprise environment with docker images from a private registry (For security reasons we are not allowed to use public registries such as gcr.io - so we pull the images and push them to our environment). The problem is that we are missing where some of the images can be configured. More specifically we would like to know where can we change the
1. gcr.io/ml-pipeline/kfp-launcher
2. Python 3.7
3. gcr.io/ml-pipeline/kfp-driver
Thank you for your help in advance. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6930 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6930/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6930/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6930/events | https://github.com/kubeflow/pipelines/issues/6930 | 1,058,496,812 | I_kwDOB-71UM4_F2Es | 6,930 | Pipeline run does not write artifacts and stop | {
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"Do you see any errors when the pipeline run? Can you share your pipeline yaml?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-19T12:52:34 | 2022-03-03T02:05:09 | null | NONE | null | Hey folks,
recently, I installed Kubeflow 1.4 with Multi User support. After a while, I managed to create a pipeline through a notebook-server.
The pipeline should consists out of different steps each with input and output artifacts.
Yet when I start the pipeline in a run, the first step does not stop nor seems to write any of the files as output-artifacts. If I look into the local minio, nothing has been written there.
Do I need to grant the user explicit access to the kubeflow minio in order to write output artifacts? And if so, how do I grand this access?
Any help is appreciated | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6928 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6928/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6928/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6928/events | https://github.com/kubeflow/pipelines/issues/6928 | 1,058,459,367 | I_kwDOB-71UM4_Fs7n | 6,928 | samples: migrate v2 samples to hermetic v2 namespace | {
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"followers_url": "https://api.githu... | null | [] | 2021-11-19T12:07:54 | 2021-11-20T18:18:36 | 2021-11-20T18:18:36 | CONTRIBUTOR | null | With https://github.com/kubeflow/pipelines/pull/6890 merged, we need to migrate v2 samples considering v2 breaking changes according to [KFP SDK 2.0: API Experience & Breaking Changes](https://docs.google.com/document/d/1nCUUVRXexXbQ0LDkGHsMIBDSu1WvJA9Upy1JzybNVMk/edit?usp=sharing).
Samples in samples/test with V2_ENGINE mode, should be migrated (copied) to samples/v2 with the same test.
Samples with V2_COMPATIBLE mode, should be cleaned up or only tested as v1 sample, according to https://github.com/kubeflow/pipelines/issues/6829.
A search of samples we need to migrate: https://github.com/search?q=PipelineExecutionMode+V2_ENGINE++repo%3Akubeflow%2Fpipelines+filename%3A_test&type=Code&ref=advsearch&l=&l= | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6927 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6927/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6927/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6927/events | https://github.com/kubeflow/pipelines/issues/6927 | 1,058,416,460 | I_kwDOB-71UM4_FidM | 6,927 | [sdk] dsl-compile-v2 fails to compile samples/v2/producer_consumer_param.py | {
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"This specific one is because of the mix use of v1 and v2 imports in the test sample file\r\nhttps://github.com/kubeflow/pipelines/blob/feebc6b66a719dc950c84500e00d23e36b37ae34/samples/v2/producer_consumer_param.py#L16-L19\r\n\r\nwhich I'm fixing in https://github.com/kubeflow/pipelines/pull/6932",
"I wonder whet... | 2021-11-19T11:14:39 | 2022-03-02T10:06:27 | null | CONTRIBUTOR | null | ### Environment
* KFP SDK version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```bash
$ cd github.com/kubeflow/pipelines
$ pip install -e sdk/python
$ cd samples/v2
$ dsl-compile-v2 --py producer_consumer_param.py --out a.json
Traceback (most recent call last):
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_data_passing.py", line 235, in serialize_value
serialized_value = serializer(value)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_data_passing.py", line 40, in _serialize_str
str(str_value), str(type(str_value))))
TypeError: Value "{{channel:task=;name=text;type=String;}}" has type "<class 'kfp.v2.components.pipeline_channel.PipelineParameterChannel'>" instead of str.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/gongyuan_kubeflow_org/miniconda3/envs/v2/bin/dsl-compile-v2", line 33, in <module>
sys.exit(load_entry_point('kfp', 'console_scripts', 'dsl-compile-v2')())
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/compiler/main.py", line 164, in main
type_check=not args.disable_type_check,
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/compiler/main.py", line 149, in compile_pyfile
type_check=type_check,
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/compiler/main.py", line 102, in _compile_pipeline_function
type_check=type_check)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 99, in compile
pipeline_parameters_override=pipeline_parameters,
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/v2/compiler/compiler.py", line 153, in _create_pipeline_v2
pipeline_func(*args_list)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/samples/v2/producer_consumer_param.py", line 61, in producer_consumer_param_pipeline
producer = producer_op(input_text=text)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_dynamic.py", line 53, in Producer
return dict_func(locals()) # noqa: F821 TODO
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_components.py", line 389, in create_task_object_from_component_and_pythonic_arguments
component_ref=component_ref,
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_components.py", line 327, in _create_task_object_from_component_and_arguments
**kwargs,
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_components.py", line 278, in _create_task_spec_from_component_and_arguments
input_type)
File "/home/gongyuan_kubeflow_org/github/kf/pipelines/sdk/python/kfp/components/_data_passing.py", line 248, in serialize_value
str(e),
ValueError: Failed to serialize the value "{{channel:task=;name=text;type=String;}}" of type "PipelineParameterChannel" to type "String". Exception: Value "{{channel:task=;name=text;type=String;}}" has type "<class 'kfp.v2.components.pipeline_channel.PipelineParameterChannel'>" instead of str.
```
### Expected result
The sample should compile.
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6926 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6926/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6926/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6926/events | https://github.com/kubeflow/pipelines/issues/6926 | 1,058,262,407 | I_kwDOB-71UM4_E82H | 6,926 | [bug] Pipeline components do not fail as expected | {
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"@pretidav Can you share your pipeline component yaml?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-19T08:16:37 | 2022-03-03T02:05:11 | null | NONE | null | ### What steps did you take
We have a pipeline with several components defined via .yaml descriptors.
### What happened:
Whenever the python code (contained into the images executed by the component) raises an error this does not translate with an error in the component execution which is still marked with a green mark.
We tried several ways to raise the error, from a simple "exit(1)" to more specific python error raising methods.
### What did you expect to happen:
We expect the component to correctly catch the error from its container execution, and stop the pipeline.
This is crucial to ensure a correct caching functionality. If the component is not failing as it should, a not-working component execution can be cached by other runs.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
part of kubeflow deployment
* KFP version:
1.7.0
* KFP SDK version:
1.8.2
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6925 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6925/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6925/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6925/events | https://github.com/kubeflow/pipelines/issues/6925 | 1,057,814,368 | I_kwDOB-71UM4_DPdg | 6,925 | [feature] <Submit spark job on AWS Databricks> | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-18T21:25:18 | 2022-03-02T10:06:29 | null | NONE | null |
There are Kubeflow pipeline Ops to submit jobs to Azure Databricks. Are we able to also submit jobs to AWS Databricks? If not, does ResourceOp support it?
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https://api.github.com/repos/kubeflow/pipelines/issues/6923 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6923/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6923/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6923/events | https://github.com/kubeflow/pipelines/issues/6923 | 1,057,805,899 | I_kwDOB-71UM4_DNZL | 6,923 | [feature] [multi-user] Pipelines Profile Controller Supports Adding Labels to Created Resources | {
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"There is no KFP profile controller image, it is by design that we allow user to customize profile controller behavior as followed.\r\n\r\nYou can change https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/base/installs/multi-user/pipelines-profile-controller/sync.py#L161 in the way that you want ... | 2021-11-18T21:13:49 | 2021-11-19T01:14:22 | 2021-11-19T01:14:22 | NONE | null | ### Feature Area
/area manifests
### What feature would you like to see?
The kubeflow pipielines deployment is placed into a cluster which has labeling requirements. The pipelines profile controller sync.py creates Kube resources in the profile's namespace. There needs to be a feature to allow adding labels to all the created resources.
### What is the use case or pain point?
The labeling requirements on the Kubernetes cluster will cause the pods to be rejected when hard enforcement in Gatekeeper is turned on.
### Is there a workaround currently?
Not currently. The only workaround is to fork the pipelines repo, build the pipelines-profile-controller image, and use that docker image.
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6922 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6922/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6922/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6922/events | https://github.com/kubeflow/pipelines/issues/6922 | 1,057,270,294 | I_kwDOB-71UM4_BKoW | 6,922 | ml-pipeline, metadata-grpc, ml-pipeline-persistentagent restart constantly | {
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"Can you look at the the yaml definition, events and logs in `ml-pipeline`? \r\n\r\n```\r\nkubectl logs my-pod --previous \r\n```\r\n\r\nhttps://kubernetes.io/docs/reference/kubectl/cheatsheet/#interacting-with-running-pods",
"> kubectl logs my-pod --previous\r\n\r\nThanks for the reply. \r\n\r\n**ml-pipelines: l... | 2021-11-18T11:56:17 | 2023-04-19T10:22:06 | 2021-11-19T15:22:14 | NONE | null | Hello everyone. We are trying to deploy KFP in an enterprise cluster. Unfortunately, the ml-pipeline, metadata-grpc, ml-pipeline-persistentagent pods are not initialized and they are constantly being restarted. Any ideas?
**ml-pipeline-persistentagent logs**
```bash
time="2021-11-18T11:51:41Z" level=fatal msg="Error creating ML pipeline API Server client: Failed to initialize pipeline client. Error: Waiting for ml pipeline API server failed after all attempts.: Get http://ml-pipeline:8888/apis/v1beta1/healthz: dial tcp 10.254.96.234:8888: connect: connection refused: Waiting for ml pipeline API server failed after all attempts.: Get http://ml-pipeline:8888/apis/v1beta1/healthz: dial tcp 10.254.96.234:8888: connect: connection refused"
```
**metadata-grpc logs**
Empty
**ml-pipeline logs**
```bash
I1118 11:48:35.998899 7 client_manager.go:160] Initializing client manager
I1118 11:48:35.998974 7 config.go:57] Config DBConfig.ExtraParams not specified, skipping
```
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https://api.github.com/repos/kubeflow/pipelines/issues/6916 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6916/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6916/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6916/events | https://github.com/kubeflow/pipelines/issues/6916 | 1,055,633,855 | I_kwDOB-71UM4-67G_ | 6,916 | [feature] Upgrade argo-workflows to 3.2.x | {
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"/assign @zijianjoy ",
"@zijianjoy I gave this a try in #6920. Let me know if I'm on the right track--or if it's easier for you to do this instead.",
"@jmcarp If you add me to edit your branch https://github.com/jmcarp/pipelines/tree/jmcarp/argo-workflows-v3.2, I can take care of the image upload part in https:... | 2021-11-17T02:27:30 | 2021-12-01T06:19:14 | 2021-12-01T06:19:14 | CONTRIBUTOR | null | ### Feature Area
/area backend
### What feature would you like to see?
Upgrade argo-workflows to 3.2.x.
### What is the use case or pain point?
argo-workflows 3.2 includes some useful features, including conditional retries.
### Is there a workaround currently?
Without conditional retries, we can either retry too often (retryPolicy: Always) and waste resources or not retry often enough (retryPolicy: OnError).
cc @zijianjoy, since I see you upgraded argo-workflows recently.
cc @jli and @joshlipschultz, who are also interested in the feature
---
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"Switch to ts-proto: https://github.com/kubeflow/pipelines/commit/d60bc99bb61a9f23fce8eabd78634236e45a5dc1\r\n\r\nWe are pending for parameter for create run to be implemented.\r\n",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity... | 2021-11-16T17:53:48 | 2022-08-24T05:40:16 | 2022-08-24T05:40:16 | COLLABORATOR | null | Reference: https://github.com/kubeflow/pipelines/pull/6804/files
Parameters change: https://github.com/neuromage/pipelines/blob/35f8448ae97c327c490a6e22159edd3280635489/samples/test/util.py#L520 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6913 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6913/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6913/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6913/events | https://github.com/kubeflow/pipelines/issues/6913 | 1,054,892,370 | I_kwDOB-71UM4-4GFS | 6,913 | Can't enable GPU inside a pod | {
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"Hey how did you solve this?",
"@kciy \r\n\r\nHey, the problem was the Kind cluster.\r\nFor some reason no pod was able to detect the GPU on the host, once I shifted to an AWS EKS the same exact configuration worked...\r\nI didn't find a way to use the \"--gpus all\" flag in Kind."
] | 2021-11-16T13:37:48 | 2021-12-20T15:28:57 | 2021-12-09T15:53:06 | NONE | null | ### Environment
Host
- AWS ec2 instance with tesla k80 gpu
- Kind cluster with 1 node
- Nvidia driver is installed and nvidia-smi cmd works on host
- Kfp version 1.8.9
### Steps to reproduce
I have a docker container that runs a train model that needs GPU.
When running on the host with the docker engine everything works fine with the cmd:
"docker run -d --gpus all <image id>"
and "nvidia-smi" works from within the container.
The model will only run if I add the "--gpus all" flag, when I run the container without this flag "nvidia-smi" doesn't work and the container won't recognize the GPU.
When running the model with kfp as an argo workflow the pod raises the same exception:
I already tries to add "set_gpu_limit(1)" to the containerOp but to no success, the pod is stuck on pending and the UI shows:
**### Question**
- **How do I enable "nvidia-smi" inside the pod through kfp** ???
- **Does this even work on KIND cluster or AWS?**
Thanks a lot!
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https://api.github.com/repos/kubeflow/pipelines/issues/6912 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6912/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6912/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6912/events | https://github.com/kubeflow/pipelines/issues/6912 | 1,054,703,867 | I_kwDOB-71UM4-3YD7 | 6,912 | [frontend] pipeline metrics not showing | {
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"@tomalbrecht This feature is only supported in v1, but not v2 compatible. ",
"@zijianjoy Could we get an update on this issue? \r\n\r\nIt looks like it received quite a few thumbs up but was not addressed in v2 compatible mode issue tacker https://github.com/kubeflow/pipelines/issues/6132\r\n\r\nI think you guys... | 2021-11-16T10:24:55 | 2023-01-18T08:53:20 | 2023-01-18T08:53:19 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? kubeflow manifests (branch 1.4)
* KFP version: SDK 1.8.9 / build version dev_local
### Steps to reproduce
- Copy/Paste Code from here: https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
- Create pipeline and create_run from component
### Expected result
- As documented here: https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/#view-the-metrics
### Materials and Reference
- I run the pipeline in 'mode=v1dsl.PipelineExecutionMode.V2_COMPATIBLE)'
- I've tested both way (json and file)
[yamls.zip](https://github.com/kubeflow/pipelines/files/7545502/yamls.zip)
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6906 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6906/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6906/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6906/events | https://github.com/kubeflow/pipelines/issues/6906 | 1,052,590,374 | I_kwDOB-71UM4-vUEm | 6,906 | 11/12/2021 Presubmit kubeflow-pipelines-tfx-python37 failure | {
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"The failure is caused by https://github.com/tensorflow/tfx/commit/f796bcb059afa612a3ea9734b7c5e58cb2f2c782 which depends on https://github.com/google/ml-metadata/commit/aa492c474f8b3a54dbb6461ddb7b35c7e69b4a47,\r\nyet none of the changes are in a released package.\r\n\r\nIdeally, the ml-metadata change should be r... | 2021-11-13T07:53:39 | 2021-11-13T09:19:40 | 2021-11-13T09:19:40 | COLLABORATOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6905/kubeflow-pipelines-tfx-python37/1459423904140365824
```
2021-11-13 07:39:25.976031: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-11-13 07:39:25.976081: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "/home/prow/go/src/github.com/kubeflow/pipelines/tfx/tfx/orchestration/kubeflow/kubeflow_dag_runner_test.py", line 23, in <module>
from tfx.components.statistics_gen import component as statistics_gen_component
File "/usr/local/lib/python3.7/site-packages/tfx/components/__init__.py", line 16, in <module>
from tfx.components.bulk_inferrer.component import BulkInferrer
File "/usr/local/lib/python3.7/site-packages/tfx/components/bulk_inferrer/component.py", line 18, in <module>
from tfx import types
File "/usr/local/lib/python3.7/site-packages/tfx/types/__init__.py", line 16, in <module>
from tfx.types.artifact import Artifact
File "/usr/local/lib/python3.7/site-packages/tfx/types/artifact.py", line 24, in <module>
from tfx.types.system_artifacts import SystemArtifact
File "/usr/local/lib/python3.7/site-packages/tfx/types/system_artifacts.py", line 38, in <module>
class Dataset(SystemArtifact):
File "/usr/local/lib/python3.7/site-packages/tfx/types/system_artifacts.py", line 40, in Dataset
MLMD_SYSTEM_BASE_TYPE = mlmd_types.Dataset().system_type
AttributeError: 'Dataset' object has no attribute 'system_type'
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6903 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6903/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6903/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6903/events | https://github.com/kubeflow/pipelines/issues/6903 | 1,052,384,784 | I_kwDOB-71UM4-uh4Q | 6,903 | [bug] concurrent.futures.process.BrokenProcessPool using ProcessPoolExecutor | {
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"u... | closed | false | null | [] | null | [
"Hello @andrijaperovic , are you able to run a sample code using ProcessPoolExecutor in an independent Kubernetes pod successfully? It doesn't seem like an issue related to Kubeflow.",
"Hi @zijianjoy , looks like the issue was caused primarily by the node pool where the pipeline task was being scheduled which has... | 2021-11-12T20:26:39 | 2021-11-19T01:59:40 | 2021-11-19T01:59:40 | NONE | null | ### What steps did you take
Running the following example code to gather output results from model training.
Results are failing to be retrieved from executor at runtime, which leads to downstream exception.
```
with ProcessPoolExecutor() as p_executor:
for i in range(nprocs):
futures.append(p_executor.submit(pool_single_model_train, Models._member_names_[i],
self.train_data, self.test_data, user_ids_test))
resultdict = {}
for f in as_completed(futures):
try:
resultdict.update(f.result())
except Exception as exc:
LOGGER.exception('Future process generated an exception: %s' % (exc))
self.score, self.count = resultdict[Models.model.name][1][0], \
resultdict[Models.model.name][1][1]
```
### What happened:
Following exception is raised in the pipeline run:
```
{"stack": "Traceback (most recent call last): File \"/trainer.py\", line 61, in __init__ resultdict.update(f.result()) File \"/opt/conda/lib/python3.7/concurrent/futures/_base.py\", line 428, in result return self.__get_result() File \"/opt/conda/lib/python3.7/concurrent/futures/_base.py\", line 384, in __get_result raise self._exception concurrent.futures.process.BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending. "}
```
### What did you expect to happen:
Result should have been completed successfully by ProcessPoolExecutor.
### Environment:
Azure AKS v1.21.2 running KFP 1.7.0-alpha.1
* How do you deploy Kubeflow Pipelines (KFP)?
https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/azure/readme.md
* KFP version:
1.7.0-alpha.1
* KFP SDK version:
1.6.2
### Anything else you would like to add:
N/A
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area components
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6901 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6901/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6901/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6901/events | https://github.com/kubeflow/pipelines/issues/6901 | 1,051,951,256 | I_kwDOB-71UM4-s4CY | 6,901 | [bug] Everything is added to one item in the artifacts menu | {
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"/assign @zijianjoy ",
"Please guide on how to provide value pipeline/workspace and name in artifact via KFP library\r\nfacing same issue",
"This is fixed by https://github.com/kubeflow/pipelines/pull/6989. We are planning to make new release for it."
] | 2021-11-12T13:08:41 | 2022-02-04T20:51:47 | null | NONE | null | ### What steps did you take
I deployed various types of pipelines,
for example
https://github.com/Building-ML-Pipelines/building-machine-learning-pipelines/tree/master/pipelines/kubeflow_pipelines
https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/mnist.ipynb
https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/kfp-v2.ipynb
https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/kfp-v2-artifact.ipynb
https://github.com/CODAIT/flight-delay-notebooks
<!-- A clear and concise description of what the bug is.-->
### What happened:
In the Artifacts menu
1. All artifacts are added to one item whose "Pipeline/Workspace" field has no value .
2. I can't select artifacts generated by [pipelines(v2)](https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/kfp-v2-artifact.ipynb) with python typing the type of inputs/outputs (Model, Dataset..) to view "Overview" and "Lineage Explorer" because they don't have a name(marked with red rectangle in the picture).
3. Unlike Kubeflow 1.3(KFP 1.6.0), [pipelines](https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/mnist.ipynb) without python typing the type of inputs/outputs (Model, Dataset..) leave no artifacts.
4. Only pipelines generated by [kubeflow_dag_runner of TFX](https://github.com/Building-ML-Pipelines/building-machine-learning-pipelines/blob/master/pipelines/kubeflow_pipelines/pipeline_kubeflow.py) have a name
In the Excutions menu
1. "Run ID/Workspace/Pipeline" fields of Excutions generated by [pipelines(v2)](https://github.com/jazzsir/kubeflow-pipelines/blob/master/simple-pipeline/kfp-v2-artifact.ipynb) with python typing the type of inputs/outputs (Model, Dataset..) have no value.
### What did you expect to happen:
In the Artifact menu
1. All artifacts are added in separate entries and their "Pipeline/Workspace" field have a value.
2. All pipelines leave artifacts
3. All items has a name so that we can select it and view "Overview" and "Lineage Explorer"
In the Excutions menu
1. All "Run ID/Workspace/Pipeline" fields of Excutions have a value.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? Full Kubeflow deployment (Kubeflow 1.4)
* KFP version: 1.7.0
* KFP SDK version: 1.7.2 and 1.8.9
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area frontend
/area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"node_id": "MDU6TGFiZWwxMjg5NTg... | closed | false | null | [] | null | [
"cc @chensun ",
"Hi @haoxins, in order not to break our existing users, we will not update the image to 3.8 until we reach a point of deprecating 3.7. Thanks for the advice!"
] | 2021-11-12T03:54:06 | 2021-12-13T19:45:26 | 2021-12-13T19:45:26 | CONTRIBUTOR | null | ### Feature Area
/area components
### What feature would you like to see?
As `Python 3.10` has released, and the `Python 3.8` has became most OS's default `Python(3)` version
So, I just advise that we should consider to upgrade the default Python version
(Such as: base images in the `Dockerfile`, default images in the output pipeline yaml)
to `3.8`
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6899 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6899/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6899/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6899/events | https://github.com/kubeflow/pipelines/issues/6899 | 1,051,386,870 | I_kwDOB-71UM4-quP2 | 6,899 | python Code sample for configmap , volume creation | {
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"Hi @kabilan6 . There aren't any specific code samples for config maps but It should work the same way as other Kubernetes objects. For creating a PVC you can leverage [VolumeOp](https://github.com/kubeflow/pipelines/tree/master/samples/core/volume_ops) which utilises ResourceOps behind the scene. You can also leve... | 2021-11-11T21:46:06 | 2022-03-02T10:06:36 | null | NONE | null |
Any code samples for configmaps, persistent volume creation ? I tried resourceop but its not working. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6896 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6896/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6896/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6896/events | https://github.com/kubeflow/pipelines/issues/6896 | 1,051,025,575 | I_kwDOB-71UM4-pWCn | 6,896 | [bug] The pipeline is terminated immediately after startup | {
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"Can you try to use emissary executor https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#emissary-executor",
"> Can you try to use emissary executor https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#emissary-executor\r\nThanks for your suggestions, I wi... | 2021-11-11T14:16:41 | 2021-11-19T06:36:17 | 2021-11-19T06:36:17 | NONE | null | ### What happened:
I created a pipeline with four components, but it was terminated immediately after it was started, and the status of the pipeline was displayed as `Unknown`.
### What did you expect to happen:
Pipeline can run normally.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I deployed it by kubeflow manifests
* KFP version:
1.7.0
* KFP SDK version:
I used the pythonSDK, the version is 1.8.9
### Anything else you would like to add:
The version of **kubernetes is v1.19.9**.
The error message is displayed as:
```
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 4s default-scheduler Successfully assigned kubeflow/pipeline-func-2qv86-3719103562 to 11.xx.x.xxx
Normal Pulled 2s kubelet Container image "gcr.io/ml-pipeline/argoexec:v3.1.6-patch-license-compliance" already present on machine
Normal Created 1s kubelet Created container wait
Warning Failed 0s kubelet Error: failed to start container "wait": Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused "process_linux.go:319: getting the final child's pid from pipe caused \"EOF\"": unknown
Normal Pulled 0s kubelet Container image "is.default.svc.explore.academy.jd.local/kubekit:0.2.3" already present on machine
Warning Failed 0s kubelet Error: cannot find volume "pipeline-runner-token-7lcgk" to mount into container "main"
```
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6895 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6895/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6895/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6895/events | https://github.com/kubeflow/pipelines/issues/6895 | 1,050,807,696 | I_kwDOB-71UM4-og2Q | 6,895 | [sdk] List of input paths is consumed as list of input values | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I'm having this problem too, any update?",
"If you are able to structure your code so that you pass a directory instead (containing t... | 2021-11-11T10:07:09 | 2022-12-23T07:43:35 | null | NONE | null | ### Environment
* KFP version: 1.6.4
* KFP SDK version: KF 1.3 release
* All dependencies version:
```
kfp==1.6.4
kfp-pipeline-spec==0.1.10
kfp-server-api==1.7.0
```
### Steps to reproduce
I'm not confident that this isn't a user error, but I haven't found any documentation that describes my use case where a component consumes a list of input paths.
1. Create file outputs that are too large to pass as values.
2. Pass them as inputs to a component that expects a list of input paths
3. The components that created the files fail with the error `This step is in Error state with this message: failed to save outputs: Request entity too large: limit is 3145728`. According to https://github.com/kubeflow/pipelines/issues/3134, this suggests that the final component is trying to consume them as values as opposed to paths.
### Expected result
The created paths should be passed as InputPaths, not as InputValues
### Materials and Reference
Source code:
```
import kfp
from kfp import dsl
from kfp.components import InputPath, OutputPath
from typing import List
def create_component_from_func():
def decorator(func):
return kfp.components.create_component_from_func(func=func)
return decorator
@create_component_from_func()
def list_of_input_paths_op(input_paths: List[InputPath]):
for p in input_paths:
print(f"Got input path {p}")
@create_component_from_func()
def create_file_op(output_path: OutputPath()):
from pathlib import Path
Path(output_path).parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "wb") as out:
out.seek((1024 * 1024 * 10) - 1)
out.write(b'\0')
@dsl.pipeline(name="list-of-input-paths", description="Demonstrates a bug")
def pipeline():
a = create_file_op()
b = create_file_op()
list_of_input_paths_op([a.output, b.output])
if __name__ == "__main__":
kfp.compiler.Compiler().compile(pipeline, "list_of_inputpaths.yml")
```
If I specify the type hint for `list_of_input_paths_op` as `List[InputPath()]` instead, I get an error:
```
TypeError: Parameters to generic types must be types. Got <kfp.components._python_op.InputPath object at 0x7fa54173caf0>.
```
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https://api.github.com/repos/kubeflow/pipelines/issues/6894 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6894/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6894/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6894/events | https://github.com/kubeflow/pipelines/issues/6894 | 1,050,695,987 | I_kwDOB-71UM4-oFkz | 6,894 | [backend] Archived Main Log File is Empty | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-11T08:23:02 | 2022-03-02T10:06:41 | null | NONE | null | ### Expected result
Log archiving with MinIO worked as expected at first but at some point the archived main logs became empty files.
The front-end displays the archived files, and these exist on MinIO, however the main logs are always empty (0 bytes) on MinIO.
The output artifacts (apart from the main logs) are correctly archived and non-empty on MinIO.
#### Logs are non-empty for pipeline run
#### Wait container shows archive occurring
Note that there is a "Successfully saved file" info log line after initiating saving the output artifact but not after initiating saving the main logs.
#### Argo Workflow shows the expected archive
#### Archived files (including main logs) display on frontend
#### Expected log files exist on MinIO but are empty for the main logs
### Steps to reproduce
Set Kubeflow to archive logs using MinIO as introduced in [this PR](https://github.com/kubeflow/pipelines/pull/2081)
i.e. set the following env vars:
In `ml-pipeline-apiserver-deployment`:
```
- name: OBJECTSTORECONFIG_BUCKETNAME
value: mlpipeline
- name: MINIO_SERVICE_SERVICE_HOST
value: minio-service.kubeflow
- name: MINIO_SERVICE_SERVICE_PORT
value: "9000"
```
In `ml-pipeline-ui-deployment`:
```
- name: ARGO_ARCHIVE_LOGS
value: "true"
- name: ARGO_ARCHIVE_ARTIFACTORY
value: minio
- name: ARGO_ARCHIVE_BUCKETNAME
value: mlpipeline
- name: ARGO_ARCHIVE_PREFIX
value: artifacts
```
This log archiving worked as expected at first but at some point the archived main logs became empty files. The output artifacts are correctly archived throughout.
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
AWS EKS
* KFP version:
1.1.0 (`988f5b0`)
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/6888 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6888/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6888/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6888/events | https://github.com/kubeflow/pipelines/issues/6888 | 1,049,318,792 | I_kwDOB-71UM4-i1WI | 6,888 | [feature] Abstract KFP Argo workflow client functions | {
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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"/cc @capri-xiyue @Bobgy ",
"/cc @james-jwu ",
"My 2 cents, we should also try to keep the interface clean enough so that not just argo or tekton but airflow or any work flow engine should be able to integrate well. ",
"> My 2 cents, we should also try to keep the interface clean enough so that not just argo ... | 2021-11-10T02:01:40 | 2022-03-02T10:06:18 | null | MEMBER | null | ### Feature Area
Currently, the KFP backend still relies heavily on the Argo golang client to run, get workflows, updates, and retry pipelines. This still remains true after implementing the v2 backend compiler because the compiled workflow needed the Argo golang client to manage the Argo workflows on Kubernetes. Since the Argo golang client needs to be part of the KFP backend code, we are proposing to abstract some common Argo client functions into a common golang interface, so other backend runtimes such as Tekton can also be plugin this way
Here is a rough docs we put up
https://docs.google.com/document/d/1Kz0WEYUesxOU8YN9QeXlsJBsalllOyr9ygpTvcGkuvA/edit#
We can take up this work if we agree on this approach.
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"In kubeflow you can use exit handler https://github.com/kubeflow/pipelines/blob/927d2a9f2dfdb90ae156979b9e0d72afa14adcd6/samples/core/exit_handler/exit_handler.py#L47. Ref: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ExitHandler",
"This issue has been automatically marked as s... | 2021-11-09T15:30:55 | 2022-03-02T11:00:52 | 2022-03-02T11:00:52 | NONE | null | ### Problem
Currently I'm having a vertex AI pipeline built using kubeflow v2 pipeline sdk (python function based). The last step of the pipeline will save the data to Big query table. I would like to trigger an existing Airflow DAG which consumes the data that is stored in Big query table saved by vertex AI pipeline. How can I trigger the Airflow DAG right after the vertex AI pipeline run was completed? How can I achieve this in kubeflow? If it is not possible in kubeflow or vertex ai, any other way of solving this problem using other of google cloud platform would be really appericiated. Suggest some solution please. | {
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"u... | closed | false | null | [] | null | [] | 2021-11-08T14:39:30 | 2021-11-23T19:35:09 | 2021-11-23T19:35:09 | CONTRIBUTOR | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
1.8.9
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
1.7.0
### Steps to reproduce
There is no concrete way to reproduce this bug. In our case, we 'd like to check a long running pipeline's status with `wait_for_run_completion`. This method tries to refresh the access token at every `55` minutes, but my access token sometimes has a shorter expiration time, e.g. 40 or 45 minutes. Then the status checking step fails with `401 Unauthorized`
The pipelines are run in AI Platform in GCP, the access tokens are generated from a service account. We have many different pipelines running under this same service account.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
`wait_for_run_completion` should work no matter how much the access token's expiration time is.
<!-- What should the correct behavior be? -->
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
I had a fix on my local to refresh the access token whenever we receive a `401 Unauthorized` error, I can open a PR with that if it makes sense.
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https://api.github.com/repos/kubeflow/pipelines/issues/6880 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6880/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6880/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6880/events | https://github.com/kubeflow/pipelines/issues/6880 | 1,047,074,979 | I_kwDOB-71UM4-aRij | 6,880 | Provide an option to increase/attach shared memory for specific ContainerOp | {
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"Not sure if there's anything special with `/dev/shm`. But KFP DSL v1 supports `add_volume` and `add_volume_mount`. Would that work for you?",
"Hi @arunnalpet, I encountered this problem too, and I resolved as follows.\r\n\r\n```python\r\nimport kfp.dsl as dsl\r\nimport kubernetes as k8s # should pip install kube... | 2021-11-08T06:41:38 | 2022-02-24T15:42:25 | null | NONE | null | ### What steps did you take
While creating a ContainerOp, there is no option to attach a volume for /dev/shm. This is needed for training pytorch models. Training with larger batch size, throws up this error. Training with tiny batch sizes will go through just fine.
### What happened:
Currently the code fails with:
`Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
`
### What did you expect to happen:
Either shared memory should be mounted automatically for container operations, or we should have an option to configure shared memory using pipeline DSL. I do not see any examples as well around this.
### Anything else you would like to add:
This is taken care while spinning up notebook instances with "Enabled Shared Memory" Option. The same option needs to be provided with ContainerOp in pipeline DSL. The same is discussed in this [issue thread](https://github.com/kubeflow/kubeflow/issues/2522#issuecomment-520190909).
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
/area sdk
<!-- /area testing -->
<!-- /area samples -->
/area components
---
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| {
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https://api.github.com/repos/kubeflow/pipelines/issues/6873 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6873/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6873/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6873/events | https://github.com/kubeflow/pipelines/issues/6873 | 1,046,085,034 | I_kwDOB-71UM4-Wf2q | 6,873 | [frontend] "Pipelines" and "Experiments" icons disappear when left sidebar is collapsed | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | closed | false | null | [] | null | [
"Thanks for the report, this is fixed in https://github.com/kubeflow/pipelines/pull/6440, I expect the next release to fix it."
] | 2021-11-05T17:33:52 | 2021-11-12T00:46:17 | 2021-11-12T00:46:17 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? standalone
* KFP version: 1.7.1
### Steps to reproduce
Collapse the left sidebar. Observe that the first 2 icons, "Pipelines" and "Experiments", aren't visible:
<img width="66" alt="Screen Shot 2021-11-05 at 13 30 20" src="https://user-images.githubusercontent.com/133466/140553518-b3eae248-ebc8-4c5f-8f6a-4ebe6279d86b.png">
### Expected result
Pipelines and Experiments icons are visible when the sidebar is collapsed.
### Materials and Reference
- When the sidebar is expanded, the icons and text appear normally.
- When the sidebar is collapsed, I can still click on the empty spaces where the icons should be, and navigate to the respective pages.
- I took a quick look at the HTML changes when the sidebar is collapsed, and didn't notice anything obviously wrong. The first 2 sidebar buttons seem to have all the same classes as the rest of the buttons when expanded and collapsed.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/6872 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6872/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6872/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6872/events | https://github.com/kubeflow/pipelines/issues/6872 | 1,045,595,790 | I_kwDOB-71UM4-UoaO | 6,872 | [bug] Recurring runs with cron trigger dissapearing | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-05T09:02:12 | 2022-03-02T10:06:47 | null | NONE | null | ### What steps did you take
I created a recurring run of a pipeline with a cron, the cron value is `0 0 0 3 * *` it is expected to run each 3rd day of each month. Here's the entire setup of the recurring run trigger :
Enabled Yes
Trigger 0 0 0 3 * *
Max. concurrent runs 1
Catchup false
Start time 02/12/2021, 15:15:00
### What happened:
When the pipeline executes, sometimes the recurring run is deleted. I cannot find what is different about the run which makes the recurring run dissapear. There is no bug happening during the pipeline execution, and all my other pipelines and recurring runs without crons are working just fine.
### What did you expect to happen:
I expect the recurring run to stay up after each run
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
KFP standalone on GKE
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.7.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
```
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I can't put any label on this issue since I don't know exactly from where it comes. Please don't hesitate to ask me for more informations if there is some I can fetch.
One thing I should note is that this issue happened several times, with several different start_time values.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6870 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6870/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6870/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6870/events | https://github.com/kubeflow/pipelines/issues/6870 | 1,045,169,610 | I_kwDOB-71UM4-TAXK | 6,870 | [frontend] Run details page prefetches all artifacts; initial page load transfers a lot of data | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | open | false | null | [] | null | [
"Hello @jli , we should probably change the fetching of all visualization artifacts when user switch to `Run output` tab on top of Run Details page. https://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/RunDetails.tsx#L581-L615",
"@zijianjoy, I also noticed that the run details page refreshes artif... | 2021-11-04T20:23:35 | 2022-03-23T06:11:04 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? our own deployment to GKE (not sure if standalone or full)
* KFP version: 1.5.1
### Steps to reproduce
background: My team has a large experiment which trains ~15 models in parallel. Each model training process includes a QC/reporting step, which generates an HTML report with various metrics and visualizations. Depending on the model and the data, the reports can be between 1MB and 30MB (they can include many images). These reports are stored on GCS and rendered as `web-app` type visualizations.
When opening the KFP runs detail page for these experiments, the frontend makes a bunch of requests to `/artifacts/get?source=minio&...mlpipeline-ui-metadata.tgz`, and then another bunch of requests to `/artifacts/get?source=gcs&...our_report_file.html`. Because these report files can be large, and there are many of them in this case, this is resulting in almost 500MB of data transfers every time someone opens the KFP UI page for this run:
<img width="1105" alt="Screen Shot 2021-11-04 at 14 52 33" src="https://user-images.githubusercontent.com/133466/140413075-387a0d20-3b16-44ae-af8e-f139b62d471f.png">
This adds load on the minio and ml-pipeline-ui services and eats bandwidth.
### Expected result
Instead of prefetching all the reports, only fetch them when the user clicks on the "Visualizations" tab.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6867 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6867/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6867/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6867/events | https://github.com/kubeflow/pipelines/issues/6867 | 1,044,536,562 | I_kwDOB-71UM4-Qlzy | 6,867 | [Python SDK] Can we upgrade the Google API Python Client packages to v2? | {
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"u... | closed | false | null | [] | null | [
"cc @chensun ",
"Hi @haoxins, thanks for opening this issue. \r\nWe're removing google-api-python-client for v2. \r\nPlease use https://github.com/googleapis/python-aiplatform for submitting pipeline jobs. "
] | 2021-11-04T09:53:14 | 2021-11-10T22:19:26 | 2021-11-10T22:19:26 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
* We should upgrade Google API packages to `v2`
### What is the use case or pain point?
* For our use cases (which the users on GCP), we will get warning messages if we use both kfp and google-api-client
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
kfp 1.8.9 requires google-api-python-client<2,>=1.7.8, but you have google-api-python-client 2.29.0 which is incompatible.
```
I think it's better to upgrade the dependency~
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/6860 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6860/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6860/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6860/events | https://github.com/kubeflow/pipelines/issues/6860 | 1,044,094,046 | I_kwDOB-71UM4-O5xe | 6,860 | 11/03/2021 Presubmit kubeflow-pipelines-tfx-python37 failing | {
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"Looks like this is related to keras 2.7.\r\nTest was passing previously when it installed keras 2.6\r\n https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6854/kubeflow-pipelines-tfx-python37/1455697869322326016",
"Root cause: https://github.com/keras-team/keras/issues/15579"
] | 2021-11-03T20:41:49 | 2021-11-03T22:51:22 | 2021-11-03T22:51:22 | COLLABORATOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6859/kubeflow-pipelines-tfx-python37/1455985756802650112
```
2021-11-03 19:55:53.822029: E tensorflow/core/lib/monitoring/collection_registry.cc:77] Cannot register 2 metrics with the same name: /tensorflow/api/keras/optimizers
Traceback (most recent call last):
File "/home/prow/go/src/github.com/kubeflow/pipelines/tfx/tfx/orchestration/kubeflow/kubeflow_dag_runner_test.py", line 23, in <module>
from tfx.components.statistics_gen import component as statistics_gen_component
File "/usr/local/lib/python3.7/site-packages/tfx/components/__init__.py", line 16, in <module>
from tfx.components.bulk_inferrer.component import BulkInferrer
File "/usr/local/lib/python3.7/site-packages/tfx/components/bulk_inferrer/component.py", line 19, in <module>
from tfx.components.bulk_inferrer import executor
File "/usr/local/lib/python3.7/site-packages/tfx/components/bulk_inferrer/executor.py", line 30, in <module>
from tfx.types import standard_component_specs
File "/usr/local/lib/python3.7/site-packages/tfx/types/standard_component_specs.py", line 16, in <module>
import tensorflow_model_analysis as tfma
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/__init__.py", line 33, in <module>
from tensorflow_model_analysis.api import tfma_unit as test
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/api/tfma_unit.py", line 71, in <module>
from tensorflow_model_analysis.api import model_eval_lib
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/api/model_eval_lib.py", line 36, in <module>
from tensorflow_model_analysis.evaluators import evaluator
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/__init__.py", line 17, in <module>
from tensorflow_model_analysis.evaluators.analysis_table_evaluator import AnalysisTableEvaluator
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/analysis_table_evaluator.py", line 27, in <module>
from tensorflow_model_analysis.evaluators import evaluator
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/evaluator.py", line 25, in <module>
from tensorflow_model_analysis.extractors import extractor
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/extractors/__init__.py", line 18, in <module>
from tensorflow_model_analysis.extractors.example_weights_extractor import ExampleWeightsExtractor
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/extractors/example_weights_extractor.py", line 27, in <module>
from tensorflow_model_analysis.extractors import extractor
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/extractors/extractor.py", line 26, in <module>
from tensorflow_model_analysis.utils import util
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/utils/__init__.py", line 21, in <module>
from tensorflow_model_analysis.utils.model_util import CombineFnWithModels
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/utils/model_util.py", line 31, in <module>
from tensorflow_model_analysis.eval_saved_model import load
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py", line 25, in <module>
from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/eval_metrics_graph/eval_metrics_graph.py", line 44, in <module>
from tensorflow_model_analysis.eval_saved_model import util
File "/usr/local/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/util.py", line 492, in <module>
checkpoint_path: Optional[Text] = None) -> Optional[bytes]:
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/util/lazy_loader.py", line 62, in __getattr__
module = self._load()
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/util/lazy_loader.py", line 45, in _load
module = importlib.import_module(self.__name__)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/tensorflow_estimator/__init__.py", line 10, in <module>
from tensorflow_estimator._api.v1 import estimator
File "/usr/local/lib/python3.7/site-packages/tensorflow_estimator/_api/v1/estimator/__init__.py", line 10, in <module>
from tensorflow_estimator._api.v1.estimator import experimental
File "/usr/local/lib/python3.7/site-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py", line 10, in <module>
from tensorflow_estimator.python.estimator.canned.dnn import dnn_logit_fn_builder
File "/usr/local/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/dnn.py", line 29, in <module>
from tensorflow_estimator.python.estimator.canned import optimizers
File "/usr/local/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/optimizers.py", line 34, in <module>
'Adagrad': tf.keras.optimizers.Adagrad,
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/util/lazy_loader.py", line 62, in __getattr__
module = self._load()
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/util/lazy_loader.py", line 45, in _load
module = importlib.import_module(self.__name__)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/keras/__init__.py", line 25, in <module>
from keras import models
File "/usr/local/lib/python3.7/site-packages/keras/models.py", line 20, in <module>
from keras import metrics as metrics_module
File "/usr/local/lib/python3.7/site-packages/keras/metrics.py", line 26, in <module>
from keras import activations
File "/usr/local/lib/python3.7/site-packages/keras/activations.py", line 20, in <module>
from keras.layers import advanced_activations
File "/usr/local/lib/python3.7/site-packages/keras/layers/__init__.py", line 23, in <module>
from keras.engine.input_layer import Input
File "/usr/local/lib/python3.7/site-packages/keras/engine/input_layer.py", line 21, in <module>
from keras.engine import base_layer
File "/usr/local/lib/python3.7/site-packages/keras/engine/base_layer.py", line 43, in <module>
from keras.mixed_precision import loss_scale_optimizer
File "/usr/local/lib/python3.7/site-packages/keras/mixed_precision/loss_scale_optimizer.py", line 18, in <module>
from keras import optimizers
File "/usr/local/lib/python3.7/site-packages/keras/optimizers.py", line 26, in <module>
from keras.optimizer_v2 import adadelta as adadelta_v2
File "/usr/local/lib/python3.7/site-packages/keras/optimizer_v2/adadelta.py", line 22, in <module>
from keras.optimizer_v2 import optimizer_v2
File "/usr/local/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py", line 37, in <module>
"/tensorflow/api/keras/optimizers", "keras optimizer usage", "method")
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/eager/monitoring.py", line 361, in __init__
len(labels), name, description, *labels)
File "/usr/local/lib/python3.7/site-packages/tensorflow/python/eager/monitoring.py", line 135, in __init__
self._metric = self._metric_methods[self._label_length].create(*args)
tensorflow.python.framework.errors_impl.AlreadyExistsError: Another metric with the same name already exists.
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6858 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6858/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6858/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6858/events | https://github.com/kubeflow/pipelines/issues/6858 | 1,043,844,498 | I_kwDOB-71UM4-N82S | 6,858 | [bug] Set a gpu limit on a ContainerOp using a pipeline parameter input | {
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"u... | closed | false | null | [] | null | [
"@Bobgy @zijianjoy Please can you take a look at this issue.",
"I think this is resolved in.the later version of the SDK.\r\n@jdalbosc-cisco Can you please upgrade your sdk and try again?",
"@chensun Thank you for your reply.\r\n\r\nI've upgraded kfp to 1.8.9 and indeed the problem is solved. The content of the... | 2021-11-03T16:55:13 | 2022-04-27T17:20:54 | 2022-02-04T20:48:33 | NONE | null | ### What steps did you take
I’m using Kfp 1.6.4 to define a pipeline. I would like to set a gpu limit on a ContainerOp using a pipeline parameter input. I have the following pipeline definition:
```
@dsl.pipeline(name="darknet-train-pipeline", description="Trains a darknet network")
def darknet_train_pipeline(...
num_gpus: int, ...) -> None:
...
training_op = kfp.components.load_component_from_file(
os.path.join(os.path.dirname(__file__), 'components/darknet_framework/training/component.yaml'))
training_task = training_op(another_task.output)
training_task.set_gpu_limit(num_gpus) # here a TypeError exception is raised
```
### What happened:
When I try to call set_gpu_limit on my ContainerOp I have the following TypeError exception:
```
File "/Users/jean-francois.dalbos/work/git/darknet_models/venv/lib/python3.9/site-packages/kfp/dsl/_container_op.py", line 380, in set_gpu_limit
self._container_spec.resources.accelerator.count = int(gpu)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'PipelineParam'
```
### What did you expect to happen:
I want Kfp to create a pipeline definition where the gpu limit depends on the pipeline input parameter named num_gpus. In the argo workflow, I would expect something like (not quite sure of the syntax though):
limits: {memory: 4G, nvidia.com/gpu: {{inputs.parameters.num_gpus}} }
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
In Kubeflow 1.3 UI, uploading the pipeline as an argo workflow yaml file.
* KFP version:
1.3
* KFP SDK version:
1.6.4
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area sdk
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/6857 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6857/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6857/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6857/events | https://github.com/kubeflow/pipelines/issues/6857 | 1,043,804,528 | I_kwDOB-71UM4-NzFw | 6,857 | [feature] Explicit list of supported GPU architectures | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | closed | false | null | [] | null | [
"@zijianjoy I've discussed this with Vedant Padwal and he thinks the sample may need to be updated.",
"@jnatale11 Can you try to run the sample to validate if this is working? I don't think there should be limitation on the types of GPUs by Kubeflow. I think the comment in this `add_node_selector_constrain` is no... | 2021-11-03T16:20:55 | 2021-11-03T21:46:30 | 2021-11-03T21:46:30 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area samples
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
I'd like to know if there's anywhere in the docs/samples which explicitly outline which GPUs are supported by Kubeflow. So far, I've found on the `kfp.dsl._container_op` [doc](https://kubeflow-pipelines.readthedocs.io/en/latest/_modules/kfp/dsl/_container_op.html) (at fn "add_node_selector_constraint") that only nvidia-tesla-k80 and tpu-v3 are supported. Yet, on the [GPU sample](https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/gpu/gpu.ipynb) there's usage of `nvidia-tesla-p100`. Could someone clarify, or update the sample?
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Clarity on which GPUs are supported
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6849 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6849/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6849/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6849/events | https://github.com/kubeflow/pipelines/issues/6849 | 1,041,875,382 | I_kwDOB-71UM4-GcG2 | 6,849 | [backend] Deprecated unittest aliases were removed in Python 3.11 | {
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"u... | open | false | null | [] | null | [
"cc @SinaChavoshi @IronPan ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-11-02T04:33:39 | 2022-03-02T15:05:10 | null | NONE | null |
* `assertDictContainsSubset` was removed.
* `assertEquals` was removed in favor `assertEqual`
```
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
components/google-cloud/tests/experimental/custom_job/unit/test_custom_job.py: self.assertDictContainsSubset(
sdk/python/kfp/containers_tests/component_builder_test.py: self.assertEquals(actual_component_yaml, self._expected_component_yaml)
```
### Expected result
The deprecated aliases should be fixed
### Materials and Reference
Ref : https://github.com/python/cpython/pull/28268 | {
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"Here's what the set of input args looks like in the console: https://screenshot.googleplex.com/HdYMJLiz8oVhxjB\r\n/cc @SinaChavoshi @IronPan ",
"@amygdala try configuring port as below - `serving_container_ports=[{\"containerPort\" : PORT}]`. The component follows k8s v1/core spec. Worked for me with above confi... | 2021-11-01T22:50:32 | 2022-02-03T12:52:24 | null | CONTRIBUTOR | null | with the v0.1.9 of `ModelUploadOp` I'm seeing the following error. I confirmed that **things work fine in 0.1.7.**
`PORT` is set as follows:
```
PORT = 8080
```
Then, in the pipeline definition, `ModelUploadOp` is configured as follows:
```
model_upload_op = gcc_aip.ModelUploadOp(
project=project,
display_name=model_display_name,
serving_container_image_uri=build_image_task.outputs['serving_container_uri'],
serving_container_predict_route="/predictions/{}".format(MAR_MODEL_NAME),
serving_container_health_route="/ping",
serving_container_ports=[PORT]
```
This is the error I see (again, I do NOT see this problem with v0.1.7):
```
2021-11-01 13:01:17.976 PDTRuntimeError: Failed to create the resource. Error: {'code': 400, 'message': "Invalid value at 'model.container_spec.ports[0]' (type.googleapis.com/google.cloud.aiplatform.v1.Port), 8080", 'status': 'INVALID_ARGUMENT', 'details': [{'@type': 'type.googleapis.com/google.rpc.BadRequest', 'fieldViolations': [{'field': 'model.container_spec.ports[0]', 'description': "Invalid value at 'model.container_spec.ports[0]' (type.googleapis.com/google.cloud.aiplatform.v1.Port), 8080"}]}]}
```
I wondered if something has changed about the expected format of this arg, but from the documentation (`serving_container_ports (Optional[Sequence[int]]=None`) it doesn't seem so. Again, this code runs fine with 0.1.7.
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https://api.github.com/repos/kubeflow/pipelines/issues/6845 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6845/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6845/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6845/events | https://github.com/kubeflow/pipelines/issues/6845 | 1,040,844,352 | I_kwDOB-71UM4-CgZA | 6,845 | [backend] severe perf problem in archive experiment API | {
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"/cc @capri-xiyue ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"We just today got hit by this issue (Kubeflow 1.3 deployment). Would really appreciate if this could get address... | 2021-11-01T07:12:28 | 2023-07-18T14:26:27 | 2023-07-18T14:26:24 | CONTRIBUTOR | null | ### Environment
* KFP version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. On a KFP cluster with many runs/experiments.
2. Use archive experiment API on any experiment, observe that the API takes >1 minute to process and may timeout + cause other queries to fail. Observed failure look like https://github.com/kubeflow/pipelines/issues/6815#issue-1037408014
The issue caused severe flakiness in test infra: https://github.com/kubeflow/pipelines/issues/6815, and was mitigated by skipping the archive experiment step in test script: https://github.com/kubeflow/pipelines/pull/6843 (We should revert the https://github.com/kubeflow/pipelines/pull/6843 after fixing the perf problem)
### Expected result
There's no perf problem with experiment archive API.
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
This is caused by a known TODO item to improve SQL perf: https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/backend/src/apiserver/storage/experiment_store.go#L266
I experimented with a SQL query improvement in https://github.com/kubeflow/pipelines/issues/6815#issuecomment-955935043, we still need to investigate how to turn that into our golang backend.
---
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"problem solved.\r\nWe fixed destination rule in Istio",
"@lightning-like could you please describe what you changed in Istio. I have the same issue.",
"@holadepo have you fixed the problem? I am facing the same issue.\r\n@lightning-like Could you explain a bit more what istio changes you made?",
"> @holadep... | 2021-10-29T19:56:35 | 2023-03-01T06:41:33 | 2021-12-04T19:40:50 | NONE | null | ### How did you deploy Kubeflow Pipelines (KFP)?
We are attempting to deploy the kubeflow pipeline over a local cent-os cluster using kustomize
We use multi-user isolation
k8s v1.19.6
On a custom Kubernetes deployment.
KFP version:
1.7.0
KFP SDK version:
kfp 1.8.6
kfp-pipeline-spec 0.1.11
kfp-server-api 1.7.0
### Steps to reproduce
For create run we use `V2 compatible` mode and default `package_root`
Try open/load/view any artefact in runs gui
```
Failed to get object in bucket mlpipeline at path v2/artifacts/pipeline/Georgy test/025b7274-585b-4aac-95c3-9ca2d609d47e/irs/output_dataset: S3Error: 503
```
Remove the ?namespace in the link then we can download the artefact.
log from ml-pipeline-ui-artifact when we try get artefact with ?namespace
```
GET /pipeline/artifacts/get?source=minio&peek=256&bucket=mlpipeline&key=v2%2Fartifacts%2Fpipeline%2FGeorgy+test%2F025b7274-585b-4aac-95c3-9ca2d609d47e%2Firs%2Fmarkdown_artifact
Getting storage artifact at: minio: mlpipeline/v2/artifacts/pipeline/Georgy test/025b7274-585b-4aac-95c3-9ca2d609d47e/irs/markdown_artifact
S3Error: 503
at getError (/server/node_modules/minio/dist/main/transformers.js:138:15)
at /server/node_modules/minio/dist/main/transformers.js:158:14
at DestroyableTransform._flush (/server/node_modules/minio/dist/main/transformers.js:80:10)
at DestroyableTransform.prefinish (/server/node_modules/readable-stream/lib/_stream_transform.js:138:10)
at DestroyableTransform.emit (events.js:223:5)
at prefinish (/server/node_modules/readable-stream/lib/_stream_writable.js:619:14)
at finishMaybe (/server/node_modules/readable-stream/lib/_stream_writable.js:627:5)
at endWritable (/server/node_modules/readable-stream/lib/_stream_writable.js:638:3)
at DestroyableTransform.Writable.end (/server/node_modules/readable-stream/lib/_stream_writable.js:594:41)
at IncomingMessage.onend (_stream_readable.js:693:10) {
code: 'UnknownError',
amzRequestid: null,
amzId2: null,
amzBucketRegion: null
}
```
log from ml-pipeline-ui when we try get artefact without ?namespace
```
GET /pipeline/artifacts/minio/mlpipeline/v2/artifacts/pipeline/Georgy%20test/025b7274-585b-4aac-95c3-9ca2d609d47e/irs/output_dataset
Getting storage artifact at: minio: mlpipeline/v2/artifacts/pipeline/Georgy test/025b7274-585b-4aac-95c3-9ca2d609d47e/irs/output_dataset
```
### Materials and Reference
We'll be grateful if you can get any clue what we can look more.
We tried to:
- env according #6750 and #4649
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https://api.github.com/repos/kubeflow/pipelines/issues/6838 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6838/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6838/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6838/events | https://github.com/kubeflow/pipelines/issues/6838 | 1,039,805,460 | I_kwDOB-71UM49-iwU | 6,838 | [bug] reenable cache v2 and importer v2 e2e test | {
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"cc @chensun @Bobgy ",
"For the cache v2, looks like the feature is working. But there is something wrong with test infra util code. It does not pass correct input to run cache\r\n\r\nFor the importer, the feature is broken\r\n\r\nFYI: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/... | 2021-10-29T17:58:22 | 2022-03-02T15:05:14 | null | CONTRIBUTOR | null | ### What steps did you take
cache e2e and importer e2e is broken after the https://github.com/kubeflow/pipelines/pull/6804 change. Need to figure out how to add protobuf.Value support in in cache and importer v2 and reenable cache v2 and importer v2 e2e test as soon as possible.
<!-- A clear and concise description of what the bug is.-->
### What happened:
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6836 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6836/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6836/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6836/events | https://github.com/kubeflow/pipelines/issues/6836 | 1,039,693,971 | I_kwDOB-71UM49-HiT | 6,836 | Unable to create experiments other namespaces in Kubeflow | {
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"cc @aronchick ",
"How are you submitting your pipeline? can you share the pipeline used?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-10-29T15:36:17 | 2022-03-02T10:06:45 | null | NONE | null | I have a Kubeflow platform installed on Azure Kubernetes that supports multi-user isolation. But still, I am not able to create experiments and submit pipelines in some other namespace. All the Pipelines are getting run in the Single kubeflow namespace. Can someone please guide me on how can I segregate the pipeline into different namespaces?
Thanks in Advance
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Is it possible for you to add post-processing step after Pipeline SDK compiles to workflow template?\r\n\r\ncc @chensun for advice.",
"Hi @jmcarp, that's an interesting idea. I wonder if a hook is added to `Compiler.compile` or `to PipelineContext`, then how different would this be from a post-compilation patchi... | 2021-10-29T02:55:13 | 2022-04-17T07:27:12 | null | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Argo workflows has features that aren't exposed in KFP, such as synchronization, template defaults, and retry expressions. If I want to use an argo feature that isn't exposed in KFP, I can file a PR against this repo and wait for a new release, or fork or patch the sdk, or subclass the compiler and override private methods. None of these seem like good options!
Instead, it would be useful if KFP included template- and workflow-level hooks allowing users to arbitrarily transform the compiled manifests. I don't have a strong opinion about where these hooks should go, but I think @jli and/or @maxhully suggested adding options to `Compiler.compile` or to `PipelineContext`.
### What is the use case or pain point?
Use argo workflows options that aren't yet supported in KFP.
### Is there a workaround currently?
Let me know if there's a better workaround, but all I've thought of so far is patching the sdk or overriding private methods.
---
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"Can we also setup a plan to refactor the documents/websites?\r\n\r\nFor me, I need introduce Kubeflow pipelines to my colleagues (most are data scientists) in my company.\r\nThe Kubeflow website still be the most important source of truth for end users.\r\nWill be very impassioned if we can update the documents to... | 2021-10-29T00:49:17 | 2022-02-03T23:07:12 | null | COLLABORATOR | null | KFP v2 compatible mode (http://bit.ly/kfp-v2-compatible) was introduced to let KFP users experiment with KFP DSL v2 features on KFP before KFP v2 engine is ready. The features is first available in KFP SDK 1.6.2. Since then, we have collected valuable user feedback which helped us evaluate our design.
Given the ongoing effort for KFP v2 engine development and the rapid development in KFP SDK v2 code base, we decide to drop v2 compatible support in KFP SDK 1.9.0 release. Users can still try out v2 compatible mode via older version of KFP SDK (the latest one with v2 compatible support is KFP SDK 1.8.7).
/cc @Bobgy
/cc @james-jwu
/cc @neuromage
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https://api.github.com/repos/kubeflow/pipelines/issues/6821 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6821/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6821/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6821/events | https://github.com/kubeflow/pipelines/issues/6821 | 1,037,909,539 | I_kwDOB-71UM493T4j | 6,821 | Automatically mount temporary directory when using the PNS executor in Argo Workflows | {
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"Hello @aoen , currently we support Emissary Executor, would you like to try that instead of PNS executor for your pipeline workflow? https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#emissary-executor.\r\n\r\nKFP doesn't guarantee PNS executor is working at this moment."
] | 2021-10-27T22:20:49 | 2021-10-29T00:02:27 | 2021-10-29T00:02:27 | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
A temporary directory should be mounted automatically by KFP when using the PNS executor in Argo Workflows.
### What is the use case or pain point?
When using PNS executor in Argo Workflows, we noticed some KFP pipelines that were writing output artifacts to /tmp/ were failing with this error: "failed to save outputs: failed to chroot to main filesystem: operation not permitted".
### Is there a workaround currently?
Yes, by adding a volume mount explicitly:
```
op = dsl.ContainerOp(....)
volume_name = "output-empty-dir"
volume = k8s_client.V1Volume(name=volume_name, empty_dir=k8s_client.V1EmptyDirVolumeSource())
volume_mount = k8s_client.V1VolumeMount(name=volume_name, mount_path=OUTPUT_DIR)
op.container.add_volume_mount(volume_mount)
op.add_volume(volume)
```
This work-around isn't great since it's not intuitive to users that they need to add this and it adds additional boilerplate to writing pipelines.
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6819 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6819/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6819/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6819/events | https://github.com/kubeflow/pipelines/issues/6819 | 1,037,787,996 | I_kwDOB-71UM4922Nc | 6,819 | issue with `google_cloud_pipeline_components` and artifact URIs | {
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"There are two aspects of this issue:\r\n\r\nUI: UI should try to exam the response code when trying to preview Artifact content, if failed, we should skip previewing this Artifact.\r\n\r\nLauncher: When trying to download artifact, if failed, continue with the execution. Because the Artifact URL is not a file.\r\n... | 2021-10-27T19:31:52 | 2022-07-22T02:36:41 | null | CONTRIBUTOR | null | This is with the 1.7 Marketplace install of KFP on a GKE cluster, and running a pipeline that uses the 1P components via v2 compat mode. I'm using v0.1.8 of the `google_cloud_pipeline_components`.
There's an apparent issue with the 1P component handling of artifact URIs in v2 compat mode. (This pipeline runs fine on Vertex Pipelines).
I believe this is new-ish behaviour. While I haven't tested with v 0.17, it would be interesting to see if that version has the same issues.
For these components, I'm seeing errors like this one for `model-upload`:
```
message: "Output Artifact "model" does not have a recognized storage URI "https://us-central1-aiplatform.googleapis.com/v1/projects/xxxxxxxx/locations/us-central1/models/5506217892460363776". Skipping uploading to remote storage."
```
The model is uploaded successfully-- it's just the Artifact handling that's not working.
Then, looking at the `Input/Output' panel, I see this, possibly related:
```
{
"error": {
"code": 401,
"message": "Request is missing required authentication credential. Expected OAuth 2 access token, login cookie or other valid authentication credential. See https://developers.google.com/identity/sign-in/web/devconsole-
...
```
I see the same for e.g. the `endpoint-create` op.
Then, a downstream op like `model-deploy` which takes as inputs the outputs from those ops, fails outright (unsurprisingly) as it can't get its inputs properly:
```
message: "Failed to execute component: failed to fetch non default buckets: failed to parse bucketConfig for output artifact "endpoint" with uri "https://us-central1-aiplatform.googleapis.com/v1/projects/xxxxxxxx/locations/us-central1/endpoints/1899964888889950208": parse bucket config failed: unsupported Cloud bucket: "https://us-central1-aiplatform.googleapis.com/v1/projects/xxxxxxxx/locations/us-central1/endpoints/1899964888889950208""
```
Note: I tried passing the default SA as an arg to the ops in case the 401 credentials issue is relevant, but that didn't seem to make a difference.
Nor did passing the default SA as an arg to `create_run_from_pipeline_func` help (though the node pool is using the same SA already).
(in fact doing that that threw a different error: `task 'keras-debug-pipeline-jwcmv.custompythonpackagetrainingjob-run' errored: pods "keras-debug-pipeline-jwcmv-2236201252" is forbidden: error looking up service account default/xxxxxxxx-compute@developer.gserviceaccount.com: serviceaccount "xxxxxxxx-compute@developer.gserviceaccount.com" not found` )
/cc @SinaChavoshi @Bobgy @IronPan
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https://api.github.com/repos/kubeflow/pipelines/issues/6818 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6818/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6818/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6818/events | https://github.com/kubeflow/pipelines/issues/6818 | 1,037,652,740 | I_kwDOB-71UM492VME | 6,818 | V2 Output Artifact Classes and Vertex Pipelines | {
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"Can you try using this approach to set artifact path?\r\n```\r\noutput_artifact.path = 'Path/to/fil'\r\n```\r\n\r\ncc @chensun for the rest of questions.",
"Hi, I tried this way, it does not throw an error and the pipeline succeeds, however the URI is not updated when I view the Artifacts/Metadata of the run in ... | 2021-10-27T17:02:50 | 2021-11-29T22:12:43 | 2021-11-29T22:12:43 | NONE | null | I am trying to create a vertex pipeline using the kfp SDK v2, I'm not sure if this is a vertex issue or a kfp issue, so forgive me if this is the wrong place for this query.
I have a reusable component in my pipeline from which I want to return a Dataset Artifact.
in the component.yaml I have the output specified:
```
outputs:
- name: model_configuration
description: output dataset describing model configuration
type: Dataset
```
and as well in the command of the yaml:
```
--model_configuration, {outputPath: model_configuration}
```
Then in the function implementing the components logic, I declare a function parameter for the output like so:
`output_model_configuration_output: Output[Dataset]`
in the Artifact types class (declared here: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/components/types/artifact_types.py) I can see there is a method for setting the path of the Artifact with `output_artifact.path('Path/to/fil')`, but when I implement this method in my code (`output_model_configuration_output.path(f"{output_path}model_configuration.parquet")`), I am met with an error:
`TypeError: 'NoneType' object is not callable`
I tried writing the URI To the artifact object's uri variable directly like so:
```output_model_configuration_output.uri = f"{output_path}model_configuration.parquet"```
This didn't throw an error, but the URI Value of the artifact object displayed in the vertex pipeline was not updated in the UI when the pipeline completed.
In addition, I tried adding some metadata to the artifact in this manner:
```output_model_configuration_output.metadata['num_rows'] = float(len(model_configuration))```
But I don't see this metadata reflected in the Vertex Pipeline UI When the pipeline run finishes, similar to the updated URI.
Let me know if there is anymore information I can provide, or if their is a more appropriate channel for this query.
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https://api.github.com/repos/kubeflow/pipelines/issues/6815 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6815/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6815/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6815/events | https://github.com/kubeflow/pipelines/issues/6815 | 1,037,408,014 | I_kwDOB-71UM491ZcO | 6,815 | [testing] kfp-ci cluster related tests flaky | {
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"/assign @capri-xiyue @Bobgy ",
"/cc @chensun ",
"Are there other causes for flakiness?",
"Saw another cause of flakiness in https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6796/kubeflow-pipelines-samples-v2/1453537694247292928\r\n\r\n```\r\nThis step is in Failed state with this... | 2021-10-27T13:13:18 | 2021-11-02T00:18:37 | 2021-11-02T00:18:37 | CONTRIBUTOR | null | [example error log](https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6746/kubeflow-pipelines-samples-v2/1450527267473068032#1:build-log.txt%3A224)
> HTTP response headers: HTTPHeaderDict({'Content-Type': 'application/json', 'Date': 'Tue, 19 Oct 2021 18:22:34 GMT', 'Vary': 'Origin', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'SAMEORIGIN', 'X-Powered-By': 'Express', 'X-Xss-Protection': '0', 'Transfer-Encoding': 'chunked', 'Set-Cookie': 'S=cloud_datalab_tunnel=HMzjedbUtOfvCUxatmKXGynHsJuhMxw5of6HG3PVShE; Path=/; Max-Age=3600'})
HTTP response body: {"error":"Failed to create a new run.: InternalServerError: Failed to store run v2-sample-test-zcmzl to table: Error 1205: Lock wait timeout exceeded; try restarting transaction","code":13,"message":"Failed to create a new run.: InternalServerError: Failed to store run v2-sample-test-zcmzl to table: Error 1205: Lock wait timeout exceeded; try restarting transaction","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error","error_details":"Failed to create a new run.: InternalServerError: Failed to store run v2-sample-test-zcmzl to table: Error 1205: Lock wait timeout exceeded; try restarting transaction"}]}
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https://api.github.com/repos/kubeflow/pipelines/issues/6813 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6813/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6813/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6813/events | https://github.com/kubeflow/pipelines/issues/6813 | 1,037,358,605 | I_kwDOB-71UM491NYN | 6,813 | [testing] v2 sample test failing at master | {
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"https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6804/kubeflow-pipelines-v2-go-test/1453335007127932928 logs all look good, but the test failed.\r\n\r\nEDIT: I was wrong, the unit test properly failed at line https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines... | 2021-10-27T12:25:54 | 2021-10-27T16:29:09 | 2021-10-27T16:17:04 | CONTRIBUTOR | null | reported by @chensun at https://github.com/kubeflow/pipelines/pull/6804#issuecomment-952542710
UPDATE: after the investigations below, it seems to me that v2 sample test is not flaky, it's basically broken at master.
The error message is https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/bea48911-71d1-42ee-9dca-530bfee1f08e
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 183, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/usr/local/lib/python3.7/runpy.py", line 109, in _get_module_details
__import__(pkg_name)
File "/usr/local/lib/python3.7/site-packages/kfp/__init__.py", line 23, in <module>
from . import dsl
File "/usr/local/lib/python3.7/site-packages/kfp/dsl/__init__.py", line 16, in <module>
from ._pipeline import Pipeline, PipelineExecutionMode, pipeline, get_pipeline_conf, PipelineConf
File "/usr/local/lib/python3.7/site-packages/kfp/dsl/_pipeline.py", line 22, in <module>
from kfp.dsl import _component_bridge
File "/usr/local/lib/python3.7/site-packages/kfp/dsl/_component_bridge.py", line 30, in <module>
from kfp.dsl import component_spec as dsl_component_spec
File "/usr/local/lib/python3.7/site-packages/kfp/dsl/component_spec.py", line 21, in <module>
from kfp.v2.components.types import type_utils
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/__init__.py", line 15, in <module>
from kfp.v2.components.experimental.yaml_component import load_component_from_text
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/experimental/yaml_component.py", line 16, in <module>
from kfp.v2.components.experimental import base_component
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/experimental/base_component.py", line 19, in <module>
from kfp.v2.components.experimental import pipeline_task
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/experimental/pipeline_task.py", line 21, in <module>
from kfp.v2.components.experimental import pipeline_channel
File "/usr/local/lib/python3.7/site-packages/kfp/v2/components/experimental/pipeline_channel.py", line 22, in <module>
from kfp.v2.components.types.experimental import type_utils
ModuleNotFoundError: No module named 'kfp.v2.components.types.experimental'
F1027 12:43:37.163580 1 main.go:50] Failed to execute component: exit status 1
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6812 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6812/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6812/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6812/events | https://github.com/kubeflow/pipelines/issues/6812 | 1,037,219,896 | I_kwDOB-71UM490rg4 | 6,812 | [bug] create component launcher configmaps/kfp-launcher : Forbidden | {
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"Sorry for interrupting. We assume the issue https://github.com/kubeflow/pipelines/pull/6530 is the same.\r\n\r\nWe have added root in the base image, it's helped"
] | 2021-10-27T10:00:36 | 2021-10-27T16:23:33 | 2021-10-27T16:23:33 | NONE | null | ### What steps did you take
Try to run `client.create_run_from_pipeline_func` from sdk with mode `kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE`
### What happened:
There is an error when we start pipeline v2 meanwhile v1 works fine
`
F1026 18:45:17.472270 61 main.go:50] Failed to create component launcher: Get " https://100.128.0.1:443/api/v1/namespaces/ruasmg5/configmaps/kfp-launcher
": Forbidden
`
### Environment:
We are attempting to deploy the kubeflow pipeline over a local cent-os cluster using kustomize
We use multi-user isolation
k8s v1.19.6
* How do you deploy Kubeflow Pipelines (KFP)?
On a custom Kubernetes deployment.
* KFP version:
1.7.0
* KFP SDK version:
kfp 1.8.6
kfp-pipeline-spec 0.1.11
kfp-server-api 1.7.0
### Anything else you would like to add:
We'll be grateful if you can get any clue what we can look more.
We tried to
get full access to serves account
predefine config map
predefine `pipene_root` at `create_run_from_pipeline_func`
### Labels
/area backend
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a :+1:. We prioritise the issues with the most :+1:. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6811 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6811/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6811/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6811/events | https://github.com/kubeflow/pipelines/issues/6811 | 1,037,218,290 | I_kwDOB-71UM490rHy | 6,811 | [bug] Kubeflow Pipeline connection error | {
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"u... | open | false | null | [] | null | [
"Hello @Deseram, there are two issues you are facing here.\r\n\r\nFor SDK issue: Please refer to https://www.kubeflow.org/docs/components/pipelines/multi-user/#when-using-the-sdk for specifying namespace when calling KFP API in multi-user mode. \r\n\r\nFor ml-pipeline-ui issue: Please check your istio configuration... | 2021-10-27T09:58:58 | 2022-03-02T15:05:15 | null | NONE | null | ### What steps did you take
We have had our kubeflow setup running for about 6 months now. But recently, we noticed that the sdk was having trouble communicating with KF Pipelines, this wasn't a regular incident. But on occasions, we would get the following errors:
`List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection","message":"List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection","code":13,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error","error_details":"List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection`
But, with the above error, the pipelines were started in the back, the sdk just failed.
---
But the most recent event was that the entire Kubeflow Pipelines UI went down, its just presented the following text:
`upstream connect error or disconnect/reset before headers. reset reason: connection failure`
Due to this being a production issue, did some investigation on the cluster itself. All the pods we healthy and didn't seem to have issues. The `ml-pipelines` pod the following errors:
```
I1027 08:05:53.056560 7 interceptor.go:29] /api.RunService/ListRuns handler starting
I1027 08:05:53.056623 7 error.go:227] Invalid input error: ListRuns must filter by resource reference in multi-user mode.
github.com/kubeflow/pipelines/backend/src/common/util.NewInvalidInputError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:174
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:76
github.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1327
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1329
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:995
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:1275
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:710
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
/api.RunService/ListRuns call failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:215
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:240
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:32
github.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1329
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:995
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:1275
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:710
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
```
and
```
I1027 08:06:05.062235 7 error.go:227] Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace.
github.com/kubeflow/pipelines/backend/src/common/util.NewInvalidInputError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:174
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:68
github.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:619
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:621
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:995
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:1275
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:710
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
/api.ExperimentService/ListExperiment call failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:215
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:240
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:32
github.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:621
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:995
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:1275
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:710
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
I1027 08:06:13.842497 7 interceptor.go:29] /api.PipelineService/ListPipelines handler starting
```
Finally, we decided to restart all `ml-pipeline-*` pods, and once restarted, the problem seems to be resolved. We suspect the issue had something to do with `istio` and the proxies for each pod were failing, but there were no logs to point these out.
We would like to ensure that we don't have similar issues happening in production again, could we please have a solution for this.
### What happened:
SKD failed with :+1:
`List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection","message":"List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection","code":13,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error","error_details":"List experiments failed.: InternalServerError: Failed to list experiments: invalid connection: invalid connection`
But, with the above error, the pipelines were started in the back, the sdk just failed.
Kubeflow UI displayed
`upstream connect error or disconnect/reset before headers. reset reason: connection failure`
### What did you expect to happen:
SDK to connect to client and start experiment
Kubeflow UI to show all pipelines and experiments.
### Environment:
- AWS
- Kubernetes version: 1.18
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.0.4
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
1.3.0
### Labels
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6810 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6810/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6810/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6810/events | https://github.com/kubeflow/pipelines/issues/6810 | 1,037,076,029 | I_kwDOB-71UM490IY9 | 6,810 | [feature] Provide custom index_url to pip when installing packages in Pipeline components | {
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"node_id": "MDU6TGFiZWwxMTM2MTE... | closed | false | null | [] | null | [
"cc @chensun ",
"Hi @valko073 , IIRC `packages_to_install` also accepts a full GitHub path, for instance `'git+https://github.com/kubeflow/pipelines#egg=kfp&subdirectory=sdk/python'`. Would that work for you?\r\n\r\nAlternatively, you may also consider containerizing your component--install the dependencies while... | 2021-10-27T07:35:03 | 2022-04-18T17:29:52 | 2022-04-18T17:29:51 | NONE | null | ### Feature Area
/area sdk
/area components
### What feature would you like to see?
As a user I would like to be able to provide a custom url to a self-hosted Pypi server for the components to install their
_packages_to_install_ from. I could not find a way of doing so in the documentation / code.
### What is the use case or pain point?
Due to Firewall restrictions, it is not possible for me to reach the official Pypi server from my Kubeflow. When manually installing packages, I can point pip via the index_url to the self-hosted Pypi inside the network. I would love to have that same functionality when starting Pipeline components.
### Is there a workaround currently?
As a workaround, we have some custom Docker images with most commonly used packages preinstalled. However, this makes the images unnecessarily large and cannot cope with specific packages not preinstalled in the images.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6799 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6799/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6799/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6799/events | https://github.com/kubeflow/pipelines/issues/6799 | 1,035,294,762 | I_kwDOB-71UM49tVgq | 6,799 | [sdk] superfluous isinstance check in local run | {
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"cc @lynnmatrix ",
"@feizerl Thanks for this issue, and you are right, the check will always be true. \r\nWhat i really want to check is whether the item is an instance of primitive type, if not, convert the item to string so that it can be used in command line.\r\n"
] | 2021-10-25T15:44:31 | 2021-12-03T18:14:58 | 2021-12-03T18:14:58 | CONTRIBUTOR | null | Hello,
I am trying to run a kubeflow pipeline locally, and am currently running into issue due to this check:
https://github.com/kubeflow/pipelines/blob/4abc4fd1874f7937a193d31dbbe650618c88ca95/sdk/python/kfp/_local_client.py#L475
My code (roughly) looks like this:
```py
@container_op
def upload(..) -> list:
...
@container_op
def download(path: String()):
...
with ParallelFor(upload.output) as path:
download(path)
```
The code works on a remote k8 run, but fails locally when run with LocalClient due to the extra layer of serialization because of the above check.
It changes path from `gs://...` to `"gs://..."`. (Note the extra pair of quotations).
What's the purpose of that check? Isn't that always going to succeed since everything is an instance of `object` in python anyway. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6793 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6793/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6793/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6793/events | https://github.com/kubeflow/pipelines/issues/6793 | 1,033,276,886 | I_kwDOB-71UM49lo3W | 6,793 | Kubeflow component does not finish executing when running on **e2-highmem-4** machine type cluster | {
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"Hello @sharmahemlata , would you like to share more information about your cluster?\r\n\r\n1. K8s version on GKE \r\n2. node image type for Nodepool `custom-2-4352` and `e2-highmem-4`\r\n3. Are you using Docker executor or Emissary Executor? https://www.kubeflow.org/docs/components/pipelines/installation/choose-ex... | 2021-10-22T07:40:58 | 2021-10-29T05:48:37 | 2021-10-29T05:48:37 | NONE | null | ### What steps did you take
On GCP, I created a kubeflow pipelines instance along with which a default cluster that uses machine **custom-2-4352** is created. I also added another node pool to the cluster of the machine type **e2-highmem-4**
### What happened:
When kubeflow component pods are assigned to the **custom-2-4352** node pool, then the kubeflow components finish executing and the output artifacts are visible.
However, when they are assigned to the **e2-highmem-4** node pool, the components do not finish. Interestingly, the logs confirm that the component code has finished executing.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)? **on GCP**
* KFP version: **1.7.0**
* KFP SDK version: **1.6.3**
### Additional Information:
I also tried to enter the pod and look for the artifacts using the kubectl command:
`kubectl exec -c main -it decision-tree-pipeline-pslcc-834569436 -- /bin/bash`
But I get this error:`
error: unable to upgrade connection: container not found ("main")`
The main container is there, the logs are available for it.
### Attachments:
I am attaching two pod definition files:
1. For a pod that runs **successfully** on the **custom-2-4352** node pool
2. For a pod that runs **unsuccessfully** on the **e2-highmem-4** node pool
### Labels
/area components
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍
[e2-highmem-4-nodepool-pod.txt](https://github.com/kubeflow/pipelines/files/7395157/e2-highmem-4-nodepool-pod.txt)
[custom-2-4352-nodepool-pod.txt](https://github.com/kubeflow/pipelines/files/7395159/custom-2-4352-nodepool-pod.txt)
.
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"The workaround is to call GET /apis/v1beta1/runs/{run_id} https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs--run_id--get and check the apiPipelineRuntime field.\r\nKFP V2 will support this feature.",
"@capri-xiyue thank you for your message,... | 2021-10-21T22:31:51 | 2022-08-11T18:23:48 | null | NONE | null | ### Feature Area
Hello
In our project, we are using kubeflow pipelines from SDK and not using UI at all. It is quite difficult to understand pipeline failure. Is there any way to retrieve component status and logs of individual pipeline run?
Maybe it is possible but I could not find it.
### What feature would you like to see?
I would like to retrieve component results and logs from pipeline SDK.
### What is the use case or pain point?
When a pipeline fails, we can't determine the reason for failure without going to kubeflow GUI.
### Is there a workaround currently?
No workaround
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-10-21T20:08:38 | 2022-03-02T15:05:25 | null | NONE | null | ### Feature Area
/area sdk
/area components
### What feature would you like to see?
Hello !
It will be so awesome to allow this kind of package install
(I know I can make my own image, but it would be easy to do a POC with this kind of import)
```python
@component(
packages_to_install = [
"pandas",
"gs://pnmsyslog-vertex-kfp-test/package/pca_aiplatform-2.tar.gz" # <-- HERE
],
)
def train_pca(
dataset: Input[Dataset],
model_artifact: Output[Model]
):
from pca_aiplatform import get_model
model = get_model()
...
```
### What is the use case or pain point?
Quickly test a package uploaded on GCS, S3 or Blobstorage
### Is there a workaround currently?
Yes 😄
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6787 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6787/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6787/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6787/events | https://github.com/kubeflow/pipelines/issues/6787 | 1,032,000,373 | I_kwDOB-71UM49gxN1 | 6,787 | mlpipeline_ui_metadata_path is not retrievable via a pipeline's run_result | {
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"We don't currently provide visualization support in local runner: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.html?highlight=run_pipeline_func_locally#kfp.run_pipeline_func_locally (local runner as Alpha). Welcome PRs if you would like to contribute.",
"Thanks for your response.\r\n\r\nI'm not... | 2021-10-21T02:49:32 | 2022-03-02T15:05:21 | null | NONE | null | ### What steps did you take & What happened
I am trying to create a python component for visualization. The component writes HTML to `mlpipeline_ui_metadata_path`. I then try to place the component in a pipeline using the local executor and run the local pipeline with idea that I can take the pipeline's run result, call `get_output_file` and check the written HTML to make sure it is what I expected. However, when I try to run this, all I see is
```
def _get_output_file_path(
self,
run_name: str,
pipeline: dsl.Pipeline,
op_name: str,
output_name: str = None,
) -> str:
"""Get the file path of component output."""
op_dependency = pipeline.ops[op_name]
if output_name is None and len(op_dependency.file_outputs) == 1:
output_name = next(iter(op_dependency.file_outputs.keys()))
> output_file = op_dependency.file_outputs[output_name]
E KeyError: None
```
Even if I try to explicitly pass `mlpipeline_ui_metadata` as the key. When I try to print out all the possible outputs for the op, I see {} even though there is an output artifact if I try to print the op object from `run_result._pipeline.ops`. An example script:
```python3
import kfp
import kfp.dsl
from kfp.components import create_component_from_func,InputPath, OutputPath
@create_component_from_func
def do_something_with_ui(mlpipeline_ui_metadata_path: OutputPath()):
import json
with open(mlpipeline_ui_metadata_path, "w") as f:
json.dump(
{
"outputs": [
{
"type": "web-app",
"storage": "inline",
"source": f"<p>Hello</p>",
}
]
},
f,
)
def test_ui():
def _pipeline():
x = do_something_with_ui()
run_result = kfp.run_pipeline_func_locally(
pipeline_func=_pipeline,
arguments={},
execution_mode=kfp.LocalClient.ExecutionMode("local"),
)
assert run_result.success
print(run_result._pipeline.ops)
output = run_result.get_output_file("do-something-with-ui")
```
### What did you expect to happen:
I expected to get the path to the mlpipeilne_ui_metadata OutputPath that I could subsequently inspect.
### Environment:
* Local Runner
* KFP version: 1.8.6
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6782 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6782/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6782/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6782/events | https://github.com/kubeflow/pipelines/issues/6782 | 1,031,780,192 | I_kwDOB-71UM49f7dg | 6,782 | [feature] viewer reconciler deletes oldest viewers down to actual MaxNumViewers number | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-10-20T19:59:13 | 2022-03-02T15:05:25 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
MaxNumViewers behavior currently only deletes the oldest viewer in a namespace when creating a new one. But it won't delete oldest viewers down to the max specified. For example, if there's already 50 viewers and you set the MaxNumViewers to 5, when you create a new one, the Reconciler will delete the oldest viewer and still leave you with 50.
Would it be an improvement to have the Reconciler delete down to the max specified? If so, I can contribute a quick, small PR.
https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/backend/src/crd/controller/viewer/reconciler/reconciler.go#L56-L61
### What is the use case or pain point?
As a user I might expect the MaxNumViewers to be the actual max and that there's garbage collection down to this number.
### Is there a workaround currently?
Manually kubectl delete viewers.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"/lifecycle frozen"
] | 2021-10-20T15:36:41 | 2022-09-30T19:26:57 | 2022-09-30T19:26:57 | COLLABORATOR | null | null | {
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] | 2021-10-20T15:36:37 | 2023-05-24T17:23:01 | 2023-05-24T17:23:01 | COLLABORATOR | null | # User Journeys
We need to validate a list of functionality regarding Pipeline Detail Page.
1. Ability to create a new run without parameter, it should be able to create a new run entity in the RunList.
2. Ability to create a new run with parameter, there are various types we should support using Protobuf.Value.
2. Ability to create a new run with custom pipeline root.
3. Ability to create recurring run.
4. Ability to upload a new version and navigate among multiple versions.
5. Ability to delete a version or a pipeline.
# Related issue
https://github.com/kubeflow/pipelines/issues/6674 | {
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"Can you help share the pipeline definition that you want to upload?",
"Example file, I've tested upload from s3 without success.\r\nPlease unpack and test .yaml file.\r\n[test_pipeline_upload.yaml.zip](https://github.com/kubeflow/pipelines/files/7440440/test_pipeline_upload.yaml.zip)\r\n\r\n",
"Bump",
"This ... | 2021-10-20T12:35:55 | 2022-04-17T06:27:24 | null | NONE | null | ### Environment
- Full Kubeflow deployment on AWS EKS, Kubeflow 1.2.0, KFP 1.0.4 or
- local Pipelines deployment on Minikube, KFP 1.6.0
On both deployments same problem
### Steps to reproduce
In Kubeflow Pipelines UI uploading pipeline by url raising an error:
<img width="593" alt="Screenshot 2021-10-20 at 12 56 45" src="https://user-images.githubusercontent.com/9049532/138093572-f94beec6-8528-4a02-89b7-b8c5804c28bc.png">
And logs from ml-pipeline pod:
_I1020 13:07:43.102924 8 error.go:227] error converting YAML to JSON: yaml: line 41: found unexpected end of stream
InvalidInputError: Failed to parse the parameter.
github.com/kubeflow/pipelines/backend/src/common/util.NewInvalidInputErrorWithDetails
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:179
github.com/kubeflow/pipelines/backend/src/common/util.ValidateWorkflow
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:51
github.com/kubeflow/pipelines/backend/src/common/util.GetParameters
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:30
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1007
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineServer).CreatePipelineVersion
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_server.go:142
github.com/kubeflow/pipelines/backend/api/go_client._PipelineService_CreatePipelineVersion_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/pipeline.pb.go:1106
main.apiServerInterceptor
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github.com/kubeflow/pipelines/backend/api/go_client._PipelineService_CreatePipelineVersion_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/pipeline.pb.go:1108
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:995
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:1275
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.23.0/server.go:710
runtime.goexit_
Problem occurs sometimes, more often for larger files.
Sometimes uploading not fails, but pipeline yaml is incomplete. Seems like a problem with connection timeouts or buffering (or something different?).
I've tried getting pipeline from AWS s3 bucket or even from local fileserver without any chunking or streaming, with same results as above.
There are no bugs while uploading from file from local machine, only from url.
### Expected result
Pipelines should be correctly uploaded by URL
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"The design is ready for review.\r\nhttps://docs.google.com/document/d/1TZeZtxwPzAImIu8Jk_e-4otSx467Ckf0smNe7JbPReE/edit?usp=sharing&resourcekey=0-lTeZGW_Ys78j1LU60CEARg",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Th... | 2021-10-20T04:55:05 | 2022-04-17T06:27:44 | null | CONTRIBUTOR | null | expand bit.ly/kfp-v2 design with control flow related detailed design | {
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] | 2021-10-19T20:50:13 | 2022-08-04T23:59:15 | 2022-08-04T23:59:15 | NONE | null | There is an error with this repository's Renovate configuration that needs to be fixed. As a precaution, Renovate will stop PRs until it is resolved.
Location: `.github/renovate.json5`
Error type: Invalid JSON5 (parsing failed)
Message: `JSON5.parse error: JSON5: invalid character '\"' at 3:3`
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https://api.github.com/repos/kubeflow/pipelines/issues/6766 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6766/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6766/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6766/events | https://github.com/kubeflow/pipelines/issues/6766 | 1,030,036,875 | I_kwDOB-71UM49ZR2L | 6,766 | [bug] Address CVE on click lower than 8.x | {
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"@chensun Can you help take a look at this? Looks like click is the sdk dependency.",
"@capri-xiyue I accidentally discovered this problem while trying to install Seldon Core ver 1.11.1, which uses click version 8.x instead.",
"This issue has been automatically marked as stale because it has not had recent act... | 2021-10-19T08:54:40 | 2022-03-18T14:23:50 | null | NONE | null | ### What happened:
A CVE issue has been reported on click version 7.x on GitHub
https://github.com/pallets/click/issues/1833
### What did you expect to happen:
Please update click to resolve it! | {
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